human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Twitter power: Tweets as electronic word of mouth"
  Abstract: In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19p of microblogs contain mention of a brand. Of the branding microblogs, nearly 20p contained some expression of brand sentiments. Of these, more than 50p were positive and 33p were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy. © 2009 Wiley Periodicals, Inc.

Connected through hyperedge HE-1 (induced by source paper "Truthy: mapping the spread of astroturf in microblog streams"):
  with members:
    - "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"
    - "Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter"
    - "Hunting for troll comments in news community forums"

Connected through hyperedge HE-2 (induced by source paper "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"):
  with members:
    - "Microblog language identification: overcoming the limitations of short, unedited and idiomatic text"
    - "Truthy: mapping the spread of astroturf in microblog streams"
    - "I can't get no sleep: discussing #insomnia on twitter"
    - "Opinion Mining and Information Fusion"
    - "NBA All-Star Prediction Using Twitter Sentiment Analysis"

Connected through hyperedge HE-3 (induced by source paper "Microblog language identification: overcoming the limitations of short, unedited and idiomatic text"):
  with members:
    - "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"
    - "Microblog-genre noise and impact on semantic annotation accuracy"

Connected through hyperedge HE-4 (induced by source paper "Entity-based Classification of Twitter Messages"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Opinion observer: analyzing and comparing opinions on the Web"):
  with members:
    - "Opinion Mining and Sentiment Analysis"
    - "Utility scoring of product reviews"
    - "Mining newsgroups using networks arising from social behavior"
    - "Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning"
    - "Combining resources to improve unsupervised sentiment analysis at aspect-level"
    - "Understand the City Better: Multimodal Aspect-Opinion Summarization for Travel"
    - "Sentiment analysis of customer reviews: balanced versus unbalanced datasets"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science