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: "equilibria of dynamic games with many players existence approximation and market structure"
  Abstract: In this paper we study stochastic dynamic games with many players; these are a fundamental model for a wide range of economic applications. The standard solution concept for such games is Markov perfect equilibrium (MPE), but it is well known that MPE computation becomes intractable as the number of players increases. We instead consider the notion of stationary equilibrium (SE), where players optimize assuming the empirical distribution of others' states remains constant at its long run average. We make two main contributions. First, we provide a rigorous justification for using SE. In particular, we provide a parsimonious collection of exogenous conditions over model primitives that guarantee existence of SE, and ensure that an appropriate approximation property to MPE holds, in a general model with possibly unbounded state spaces. Second, we draw a significant connection between the validity of SE, and market structure: under the same conditions that imply SE exist and approximates MPE well, the market becomes fragmented in the limit of many firms. To illustrate this connection, we study in detail a series of dynamic oligopoly examples. These examples show that our conditions enforce a form of "decreasing returns to larger states"; this yields fragmented industries in the limit. By contrast, violation of these conditions suggests "increasing returns to larger states" and potential market concentration. In that sense, our work uses a fully dynamic framework to also contribute to a longstanding issue in industrial organization: understanding the determinants of market structure in different industries.

Connected through hyperedge HE-1 (induced by source paper "signaling equilibria in mean field games"):
  with members:
    - "decentralized bayesian learning in dynamic games"
    - "dynamic games with asymmetric information common information based perfect bayesian equilibria and sequential decomposition"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-2 (induced by source paper "markov perfect equilibria in non stationary mean field games"):
  with members:
    - "decentralized bayesian learning in dynamic games"
    - "dynamic games with asymmetric information common information based perfect bayesian equilibria and sequential decomposition"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for mean field game"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "energy coupon a mean field game perspective on demand response in smart grids"

Connected through hyperedge HE-4 (induced by source paper "mean field equilibria for resource competition in spatial settings"):
  with members:
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "incentivizing sharing in realtime d2d streaming networks a mean field game perspective"
    - "the supermarket game"

Connected through hyperedge HE-5 (induced by source paper "mean field equilibria for competitive exploration in resource sharing settings"):
  with members:
    - "the supermarket game"

Connected through hyperedge HE-6 (induced by source paper "learning and efficiency in games with dynamic population"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "achieving all with no parameters adaptive normalhedge"
    - "inducing approximately optimal flow using truthful mediators"
    - "private pareto optimal exchange"
    - "mirror descent meets fixed share and feels no regret"
    - "bayesian games and the smoothness framework"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "mean field games in nudge systems for societal networks"):
  with members:
    - "incentivizing sharing in realtime d2d streaming networks a mean field game perspective"
    - "energy coupon a mean field game perspective on demand response in smart grids"
    - "integrating energy storage into the smart grid a prospect theoretic approach"
    - "incentive mechanisms for internet congestion management fixed budget rebate versus time of day pricing"
    - "a mean field game approach to scheduling in cellular systems"

Connected through hyperedge HE-8 (induced by source paper "a mean field game approach to scheduling in cellular systems"):
  with members:
    - "the supermarket game"

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)