input: Write a python function to classify sentiment of a given sentence using NLTK.
  assert classify_sentiment('I am feeling really good today!') == 'Positive'
  assert classify_sentiment('This is a terrible day.') == 'Negative'
  assert classify_sentiment('I am unsure about how I feel.') == 'Neutral'
output: def classify_sentiment(sentence):
      import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer
      
      nltk.download('vader_lexicon', quiet=True)
      sia = SentimentIntensityAnalyzer()
      
      sentiment_scores = sia.polarity_scores(sentence)
      compound_score = sentiment_scores['compound']
      
      if compound_score >= 0.05:
          return 'Positive'
      elif compound_score <= -0.05:
          return 'Negative'
      else:
          return 'Neutral'