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: "opinion influence and evolution in social networks a markovian agents model"
  Abstract: This paper investigates on the effect on collective opinions of filtering algorithms managed by social network platforms. For this purpose, a stochastic multi-agent model for opinion dynamics that explicitly accounts for a centralized tuning of the strength of interaction between individuals within a social network is proposed. The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected through influence parameters, by the opinions of the neighbors. The properties of this model are studied in a general setting as well as in interesting special cases. A general result is that the overall model of the social network behaves like a high-dimensional Markov chain, which is viable to Monte Carlo simulation. Under the assumption of identical agents and unbiased influence, the model can be marginalized, thus implying, among other things, that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion. Moreover, a detailed analysis is carried out in the case of the so-called Peer Assembly, which describes the evolution of binary opinions among identical agents whose interaction of graph is completely connected. It is shown that the Peer Assembly can be lumped into a birth-death chain that can be given a complete analytical characterization. Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours, depending on the interaction strength parameters.

Connected through hyperedge HE-1 (induced by source paper "the closed loop between opinion formation and personalised recommendations"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "the limits of popularity based recommendations and the role of social ties"
    - "biased assimilation homophily and the dynamics of polarization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

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)