input: Write a python function to analyze sentiment of given sentences using NLTK's VADER SentimentIntensityAnalyzer.
  assert analyze_sentiment(["This article is bad"])==["Negative"]
  assert analyze_sentiment(["I love this place!", "I hate this place!"])==["Positive", "Negative"]
  assert analyze_sentiment(["This is okay.", "I'm not sure how I feel about this."])==["Neutral", "Neutral"]
output: def analyze_sentiment(sentences):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      nltk.download('vader_lexicon')
      
      sid = SentimentIntensityAnalyzer()
      results = []
      
      for sentence in sentences:
          score = sid.polarity_scores(sentence)
          if score['compound'] >= 0.05:
              results.append('Positive')
          elif score['compound'] <= -0.05:
              results.append('Negative')
          else:
              results.append('Neutral')
      
      return results