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: "analysis and control of beliefs in social networks"
  Abstract: In this paper, we investigate the problem of how beliefs diffuse among members of social networks. We propose an information flow model (IFM) of belief that captures how interactions among members affect the diffusion and eventual convergence of a belief. The IFM model includes a generalized Markov Graph (GMG) model as a social network model, which reveals that the diffusion of beliefs depends heavily on two characteristics of the social network characteristics, namely degree centralities and clustering coefficients. We apply the IFM to both converged belief estimation and belief control strategy optimization. The model is compared with an IFM including the Barabasi-Albert model, and is evaluated via experiments with published real social network data.

Connected through hyperedge HE-1 (induced by source paper "active sensing of social networks"):
  with members:
    - "signal processing on graphs modeling causal relations in big data"
    - "the social system identification problem"
    - "graph theoretic distributed inference in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "social structure of facebook networks"
    - "a unique nonnegative solution to an underdetermined system from vectors to matrices"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-2 (induced by source paper "the social system identification problem"):
  with members:
    - "active sensing of social networks"
    - "efficiently learning ising models on arbitrary graphs"
    - "graph theoretic distributed inference in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-3 (induced by source paper "boundary value problems in consensus networks"):
  with members:
    - "gossip algorithms for distributed signal processing"

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)