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: "parameterized centrality metric for network analysis"
  Abstract: USC Information Sciences Institute4676 Admiralty Way, Marina del Rey, CA 90292(Dated: October 21, 2010)A variety of metrics have been proposed to measure the relative importance of nodes in a net-work. One of these, -centrality [1], measures the number of attenuated paths that exist betweennodes. We introduce a normalized version of this metric and use it to study network structure,speci cally, to rank nodes and nd community structure of the network. Speci cally, we extend themodularity-maximization method [2] for community detection to use this metric as the measure ofnode connectivity. Normalized -centrality is a powerful tool for network analysis, since it containsa tunable parameter that sets the length scale of interactions. By studying how rankings and dis-covered communities change when this parameter is varied allows us to identify locally and globallyimportant nodes and structures. We apply the proposed method to several benchmark networksand show that it leads to better insight into network structure than alternative methods.

Connected through hyperedge HE-1 (induced by source paper "community detection via katz and eigenvector centrality"):
  with members:
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "information is not a virus and other consequences of human cognitive limits"):
  with members:
    - "the majority illusion in social networks"
    - "user effort and network structure mediate access to information in networks"
    - "the simple rules of social contagion"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "network weirdness exploring the origins of network paradoxes"
    - "friendship paradox redux your friends are more interesting than you"
    - "stochastic models predict user behavior in social media"
    - "information diffusion and external influence in networks"

Connected through hyperedge HE-3 (induced by source paper "the interplay between dynamics and networks centrality communities and cheeger inequality"):
  with members:
    - "spectral clustering with epidemic diffusion"

Connected through hyperedge HE-4 (induced by source paper "limited attention and centrality in social networks"):
  with members:
    - "google matrix of twitter"
    - "rethinking centrality the role of dynamical processes in social network analysis"
    - "non conservative diffusion and its application to social network analysis"
    - "predicting influential users in online social networks"
    - "crowdsourcing attention and productivity"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "a survey of data mining techniques for social media analysis"):
  with members:
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "mining the web for synonyms pmi ir versus lsa on toefl"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-6 (induced by source paper "spectral clustering with epidemic diffusion"):
  with members:
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "rethinking centrality the role of dynamical processes in social network analysis"):
  with members:
    - "the simple rules of social contagion"
    - "social dynamics of digg"
    - "what stops social epidemics"
    - "entropy based classification of retweeting activity on twitter"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "a framework for quantitative analysis of cascades on networks"
    - "influence and passivity in social media"
    - "using stochastic models to describe and predict social dynamics of web users"

Connected through hyperedge HE-8 (induced by source paper "non conservative diffusion and its application to social network analysis"):
  with members:
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "using a model of social dynamics to predict popularity of news"
    - "a framework for quantitative analysis of cascades on networks"
    - "predicting influential users in online social networks"

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)