import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import linear_sum_assignment

a = input().split(" ")
n_books = int(a[0])
n_libs = int(a[1])
n_days = int(a[2])
books = list(map(int, input().split(" ")))
books_used = [ False ] * n_books
lib_info = []
lib_contents = []
for i in range(n_libs):
    lib_info.append(list(map(int, input().split(" "))))
    lib_contents.append(list(map(int, input().split(" "))))

lib_signup = [ False ] * n_libs

lib_values = []
for lib in lib_contents:
    values = 0
    for book in lib:
        values += books[book]
    lib_values.append(values)

def find_book(lib):
    global lib_info, n_books, lib_contents, books_used
    for i in range(lib_info[lib][2]):
        for book_id in range(n_books):
            if book_id in lib_contents[lib] and not books_used[book_id]:
                return book_id
    return -1

signed_up_libs = []
books_per_lib = [[] for i in range(n_libs)]
signup_left = 0
signup_lib = 0
for day in range(n_days):
    if signup_left == 0:
        # Find not yet signed up library
        for i in range(n_libs):
            if not lib_signup[i]:
                signup_left = lib_info[i][1]
                signup_id = i
                break
    else:
        if signup_left == 1:
            lib_signup[signup_lib] = True
            signed_up_libs.append(signup_lib)
        signup_left -= 1

    for lib in range(n_libs):
        print("Lib: " + str(lib))
        if lib_signup[lib]:
            book_id = find_book(lib)
            if book_id >= 0:
                print("book_id: " + str(book_id))
                books_per_lib[lib].append(book_id)
                books_used[book_id] = True

print(signed_up_libs)
print(books_per_lib)