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: "predicting influential users in online social networks"
  Abstract: Who are the influential people in an online social network? The answer to this question depends not only on the structure of the network, but also on details of the dynamic processes occurring on it. We classify these processes as conservative and non-conservative. A random walk on a network is an example of a conservative dynamic process, while information spread is non-conservative. The influence models used to rank network nodes can be similarly classified, depending on the dynamic process they implicitly emulate. We claim that in order to correctly rank network nodes, the influence model has to match the details of the dynamic process. We study a real-world network on the social news aggregator Digg, which allows users to post and vote for news stories. We empirically define influence as the number of in-network votes a user's post generates. This influence measure, and the resulting ranking, arises entirely from the dynamics of voting on Digg, which represents non-conservative information flow. #R##N#We then compare predictions of different influence models with this empirical estimate of influence. The results show that non-conservative models are better able to predict influential users on Digg. We find that normalized alpha-centrality metric turns out to be one of the best predictors of influence. We also present a simple algorithm for computing this metric and the associated mathematical formulation and analytical proofs.

Connected through hyperedge HE-1 (induced by source paper "sensing and modeling human behavior using social media and mobile data"):
  with members:
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "hoodsquare modeling and recommending neighborhoods in location based social networks"

Connected through hyperedge HE-2 (induced by source paper "distinguishing between personal preferences and social influence in online activity feeds"):
  with members:
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"
    - "friends strangers and the value of ego networks for recommendation"
    - "the role of social networks in information diffusion"
    - "social influence in social advertising evidence from field experiments"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-3 (induced by source paper "finding influential users in social media using association rule learning"):
  with members:
    - "measuring user influence on twitter"
    - "predicting community evolution in social networks"
    - "key user extraction based on telecommunication data aka key users in social network how to find them"
    - "the multidimensional study of viral campaigns as branching processes"

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"
    - "parameterized centrality metric for network analysis"
    - "non conservative diffusion and its application to social network analysis"
    - "crowdsourcing attention and productivity"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "social contagion an empirical study of information spread on digg and twitter follower graphs"):
  with members:
    - "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"
    - "using a model of social dynamics to predict popularity of news"
    - "a framework for quantitative analysis of cascades on networks"
    - "stochastic models of user contributory web sites"
    - "analysis of social voting patterns on digg"

Connected through hyperedge HE-6 (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"
    - "parameterized centrality metric for network analysis"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "information theoretic measures of influence based on content dynamics"):
  with members:
    - "echoes of power language effects and power differences in social interaction"
    - "information transfer in social media"
    - "mark my words linguistic style accommodation in social media"
    - "influence and passivity in social media"

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"
    - "parameterized centrality metric for network analysis"
    - "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"

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.CY(Computers and Society)