human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "tripartite graph clustering for dynamic sentiment analysis on social media"
  Abstract: The growing popularity of social media (e.g., Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised tri-clustering framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the proposed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California ballot Twitter data.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous information network model for equipment standard system"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "label propagation on k partite graphs with heterophily"):
  with members:
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "a survey of heterogeneous information network analysis"
    - "joint inference of multiple label types in large networks"
    - "linearized and single pass belief propagation"
    - "learning under concept drift an overview"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "scalable temporal latent space inference for link prediction in dynamic social networks"):
  with members:
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "nonparametric link prediction in dynamic networks"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "the darpa twitter bot challenge"):
  with members:
    - "people are strange when you re a stranger impact and influence of bots on social networks"
    - "entropy based classification of retweeting activity on twitter"
    - "truthy mapping the spread of astroturf in microblog streams"
    - "information transfer in social media"

Connected through hyperedge HE-5 (induced by source paper "community detection in political twitter networks using nonnegative matrix factorization methods"):
  with members:
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-6 (induced by source paper "latent space model for road networks to predict time varying traffic"):
  with members:
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "shortest path and distance queries on road networks an experimental evaluation"

Connected through hyperedge HE-7 (induced by source paper "twitter sentiment analysis lexicon method machine learning method and their combination"):
  with members:
    - "twitter sentiment analysis applied to finance a case study in the retail industry"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.SI(Social and Information Networks)