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: "reaching consensus via non bayesian asynchronous learning in social networks"
  Abstract: We study the outcomes of information aggregation in online social networks. Our main result is that networks with certain realistic structural properties avoid information cascades and enable a population to effectively aggregate information. In our model, each individual in a network holds a private, independent opinion about a product or idea, biased toward a ground truth. Individuals declare their opinions asynchronously, can observe the stated opinions of their neighbors, and are free to update their declarations over time. Supposing that individuals conform with the majority report of their neighbors, we ask whether the population will eventually arrive at consensus on the ground truth. We show that the answer depends on the network structure: there exist networks for which consensus is unlikely, or for which declarations converge on the incorrect opinion with positive probability. On the other hand, we prove that for networks that are sparse and expansive, the population will converge to the correct opinion with high probability.

Connected through hyperedge HE-1 (induced by source paper "asynchronous majority dynamics in preferential attachment trees"):
  with members:
    - "stabilizing consensus with many opinions"
    - "learning in hierarchical social networks"
    - "subexponential convergence for information aggregation on regular trees"

Connected through hyperedge HE-2 (induced by source paper "resource allocation game on social networks best response dynamics and convergence"):
  with members:
    - "fast convergence of regularized learning in games"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"
    - "on discrete preferences and coordination"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-3 (induced by source paper "opinion dynamics in networks convergence stability and lack of explosion"):
  with members:
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-4 (induced by source paper "discrete preference games social influence through coordination and beyond"):
  with members:
    - "short sequences of improvement moves lead to approximate equilibria in constraint satisfaction games"
    - "decentralized dynamics for finite opinion games"
    - "on discrete preferences and coordination"
    - "how bad is forming your own opinion"
    - "efficient computation of approximate pure nash equilibria in congestion games"

Connected through hyperedge HE-5 (induced by source paper "minority becomes majority in social networks"):
  with members:
    - "decentralized dynamics for finite opinion games"
    - "on discrete preferences and coordination"
    - "how bad is forming your own opinion"

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.GT(Computer Science and Game Theory)