input: from typing import List
  
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """
      Classify the sentiment of a list of movie reviews using Natural Language Processing.
      Each review will be analyzed and categorized as 'negative' or 'positive' based on
      the sentiment polarity score from the VADER sentiment analysis tool.
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['negative']
      >>> classify_sentiment(['It was an amazing experience!', 'Terrible movie, waste of time.'])
      ['positive', 'negative']
      """
      
      nltk.download('vader_lexicon')
      sid = vader.SentimentIntensityAnalyzer()
      sentiment_labels = []
  
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiment_labels.append('negative')
          else:
              sentiment_labels.append('positive')
  
      return sentiment_labels
output:     import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer as vader