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: "removal of data incest in multi agent social learning in social networks"
  Abstract: Motivated by online reputation systems, we investigate social learning in a network where agents interact on a time dependent graph to estimate an underlying state of nature. Agents record their own private observations, then update their private beliefs about the state of nature using Bayes' rule. Based on their belief, each agent then chooses an action (rating) from a finite set and transmits this action over the social network. An important consequence of such social learning over a network is the ruinous multiple re-use of information known as data incest (or mis-information propagation). In this paper, the data incest management problem in social learning context is formulated on a directed acyclic graph. We give necessary and sufficient conditions on the graph topology of social interactions to eliminate data incest. A data incest removal algorithm is proposed such that the public belief of social learning (and hence the actions of agents) is not affected by data incest propagation. This results in an online reputation system with a higher trust rating. Numerical examples are provided to illustrate the performance of the proposed optimal data incest removal algorithm.

Connected through hyperedge HE-1 (induced by source paper "interactive sensing and decision making in social networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "tracking the empirical distribution of a markov modulated duplication deletion random graph"
    - "diffusion adaptation over networks"
    - "twitter mood predicts the stock market"
    - "a game theoretic analysis of incentives in content production and sharing over peer to peer networks"
    - "game theory and the frequency selective interference channel"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-2 (induced by source paper "information diffusion in social sensing"):
  with members:
    - "forecasting popularity of videos in youtube"
    - "interactive sensing and decision making in social networks"
    - "loopy belief propagation for approximate inference an empirical study"
    - "early stage influenza detection from twitter"
    - "twitter mood predicts the stock market"

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)