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: "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment"
  Abstract: Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.

Connected through hyperedge HE-1 (induced by source paper "Truthy: mapping the spread of astroturf in microblog streams"):
  with members:
    - "Twitter power: Tweets as electronic word of mouth"
    - "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 "I can't get no sleep: discussing #insomnia on twitter"):
  with members:
    - "I Didn't Sign Up for This!: Informed Consent in Social Network Research"

Connected through hyperedge HE-3 (induced by source paper "Microblog language identification: overcoming the limitations of short, unedited and idiomatic text"):
  with members:
    - "Twitter power: Tweets as electronic word of mouth"
    - "Microblog-genre noise and impact on semantic annotation accuracy"

Connected through hyperedge HE-4 (induced by source paper "NBA All-Star Prediction Using Twitter Sentiment Analysis"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Opinion Mining and Information Fusion"):
  with members:
    - "Opinion Mining and Sentiment Analysis"
    - "Using Deep Learning to Predict Sentiments: Case Study in Tourism"

Connected through hyperedge HE-6 (induced by source paper "Twitter power: Tweets as electronic word of mouth"):
  with members:
    - "Microblog language identification: overcoming the limitations of short, unedited and idiomatic text"
    - "Truthy: mapping the spread of astroturf in microblog streams"
    - "Opinion observer: analyzing and comparing opinions on the Web"
    - "Entity-based Classification of Twitter Messages"
    - "Social Media Campaigns For New Product Introductions"
    - "The Catalyzing Factors of Official Documents Exchange via Microblogging in Public Sectors: A Case Study based on the T-O-E Framework"
    - "Analyzing Second Screen Based Social Soundtrack of TV Viewers from Diverse Cultural Settings"

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