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: "User-level sentiment analysis incorporating social networks"
  Abstract: We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.

Connected through hyperedge HE-1 (induced by source paper "Opinion Mining and Sentiment Analysis"):
  with members:
    - "Using Universal Linguistic Knowledge to Guide Grammar Induction"
    - "Opinion Mining and Information Fusion"
    - "Sentiment analysis in Facebook and its application to e-learning"
    - "Utility scoring of product reviews"
    - "Opinion observer: analyzing and comparing opinions on the Web"
    - "Mining newsgroups using networks arising from social behavior"
    - "Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior"
    - "Active Learning for Imbalanced Sentiment Classification"

Connected through hyperedge HE-2 (induced by source paper "Mining newsgroups using networks arising from social behavior"):
  with members:
    - "Opinion Mining and Sentiment Analysis"
    - "How good is the Goemans-Williamson MAX CUT algorithm?"
    - "Opinion observer: analyzing and comparing opinions on the Web"

Connected through hyperedge HE-3 (induced by source paper "PopMeter: Linked-Entities in a Sentiment Graph"):
  with members:
    (none)

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