import sys
 
def solve():
    # 標準入力からデータを読み込む
    input_lines = sys.stdin.read().split('\n')
 
    # 空行を除去して整理する
    lines = [line.strip() for line in input_lines if line.strip()]
 
    if len(lines) < 3:
        return
 
    # 1行目: ナップサックの容量 W
    W = int(lines[0])
 
    # 2行目: 各アイテムの大きさ（重さ）のリスト
    weights = list(map(int, lines[1].split()))
 
    # 3行目: 各アイテムの価値のリスト
    values = list(map(int, lines[2].split()))
 
    # アイテムの数
    n = len(weights)
 
    # DPテーブルの作成
    # dp[i][j] は、i個までのアイテムを使って容量jの時に得られる最大価値
    # iは 0 から n, j は 0 から W までのサイズを持つ2次元配列
    dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
 
    # 動的計画法の実行
    for i in range(1, n + 1):
        item_weight = weights[i-1]  # 現在のアイテムの重さ (0-indexなので-1)
        item_value = values[i-1]    # 現在のアイテムの価値 (0-indexなので-1)
 
        for j in range(W + 1):
            if j < item_weight:
                # 容量不足で現在のアイテムが入らない場合、前の状態を引き継ぐ
                dp[i][j] = dp[i-1][j]
            else:
                # アイテムを入れる場合と入れない場合で価値が大きい方を選ぶ
                # OPT(i, j) = max( OPT(i-1, j), OPT(i-1, j - weight) + value )
                dp[i][j] = max(dp[i-1][j], dp[i-1][j - item_weight] + item_value)
 
    # 最終的な解 OPT(n, W) を出力
    print(dp[n][W])
 
if __name__ == '__main__':
    solve()

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