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 fluctuations and disagreement in social networks"
  Abstract: We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves an inhomogeneous stochastic gossip process of continuous opinion dynamics in a society consisting of two types of agents: 1 regular agents who update their beliefs according to information that they receive from their social neighbors and 2 stubborn agents who never update their opinions and might represent leaders, political parties, or media sources attempting to influence the beliefs in the rest of the society. When the society contains stubborn agents with different opinions, the belief dynamics never lead to a consensus among the regular agents. Instead, beliefs in the society fail to converge almost surely, the belief profile keeps on fluctuating in an ergodic fashion, and it converges in law to a nondegenerate random vector.#R##N##R##N#The structure of the graph describing the social network and the location of the stubborn agents within it shape the opinion dynamics. The expected belief vector is proved to evolve according to an ordinary differential equation coinciding with the Kolmogorov backward equation of a continuous-time Markov chain on the graph with absorbing states corresponding to the stubborn agents, and hence to converge to a harmonic vector, with every regular agent's value being the weighted average of its neighbors' values, and boundary conditions corresponding to the stubborn agents' beliefs. Expected cross products of the agents' beliefs allow for a similar characterization in terms of coupled Markov chains on the graph describing the social network.#R##N##R##N#We prove that, in large-scale societies, which are highly fluid, meaning that the product of the mixing time of the Markov chain on the graph describing the social network and the relative size of the linkages to stubborn agents vanishes as the population size grows large, a condition of homogeneous influence emerges, whereby the stationary beliefs' marginal distributions of most of the regular agents have approximately equal first and second moments.

Connected through hyperedge HE-1 (induced by source paper "shifting opinions in a social network through leader selection"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "an efficient parallel solver for sdd linear systems"
    - "submodular optimization with submodular cover and submodular knapsack constraints"

Connected through hyperedge HE-2 (induced by source paper "disagreement and polarization in two party social networks"):
  with members:
    - "shifting opinions in a social network through leader selection"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-3 (induced by source paper "asymptotic properties of distributed social sampling algorithm"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "consensus conditions of continuous time multi agent systems with time delays and measurement noises"
    - "ergodic randomized algorithms and dynamics over networks"
    - "network based consensus averaging with general noisy channels"

Connected through hyperedge HE-4 (induced by source paper "polarization and fluctuations in signed social networks"):
  with members:
    - "convergence properties of the heterogeneous deffuant weisbuch model"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "dynamics over signed networks"

Connected through hyperedge HE-5 (induced by source paper "maintaining ferment on opinion control over social networks"):
  with members:
    - "discrete time optimal control with frequency constraints for non smooth systems"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "how to run a campaign optimal control of sis and sir information epidemics"

Connected through hyperedge HE-6 (induced by source paper "occam s razor in opinion dynamics the weighted median influence process"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "how bad is forming your own opinion"

Connected through hyperedge HE-7 (induced by source paper "finite time influence systems and the wisdom of crowd effect"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"

Connected through hyperedge HE-8 (induced by source paper "structured networks and coarse grained descriptions a dynamical perspective"):
  with members:
    - "multiscale dynamical embeddings of complex networks"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectral redemption in clustering sparse 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)