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: "literature survey on interplay of topics information diffusion and connections on social networks"
  Abstract: Researchers have attempted to model information diffusion and topic trends and lifecycle on online social networks. They have investigated the role of content, social connections and communities, familiarity and behavioral similarity in this context. The current article presents a survey of representative models that perform topic analysis, capture information diffusion, and explore the properties of social connections in the context of online social networks. The article concludes with a set of outlines of open problems and possible directions of future research interest. This article is intended for researchers to identify the current literature, and explore possibilities to improve the art.

Connected through hyperedge HE-1 (induced by source paper "topic lifecycle on social networks analyzing the effects of semantic continuity and social communities"):
  with members:
    - "the role of social networks in information diffusion"
    - "information diffusion and external influence in networks"

Connected through hyperedge HE-2 (induced by source paper "global rank estimation"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "group centrality for semantic networks a swot analysis featuring random walks"
    - "centrality measures in networks based on nodes attributes long range interactions and group influence"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"
    - "fully dynamic approximation of betweenness centrality"

Connected through hyperedge HE-3 (induced by source paper "emtagger a word embedding based novel method for hashtag recommendation on twitter"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "trends in social media persistence and decay"

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)