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: "spectrum sharing as spatial congestion games"
  Abstract: In this paper, we present and analyze the properties of a new class of games - the spatial congestion game (SCG), which is a generalization of the classical congestion game (CG). In a classical congestion game, multiple users share the same set of resources and a user's payoff for using any resource is a function of the total number of users sharing it. As a potential game, this game enjoys some very appealing properties, including the existence of a pure strategy Nash equilibrium (NE) and that every improvement path is finite and leads to such a NE (also called the finite improvement property or FIP). While it's tempting to use this model to study spectrum sharing, it does not capture the spatial reuse feature of wireless communication, where resources (interpreted as channels) may be reused without increasing congestion provided that users are located far away from each other. This motivates us to study an extended form of the congestion game where a user's payoff for using a resource is a function of the number of its interfering users sharing it. This naturally results in a spatial congestion game (SCG), where users are placed over a network (or a conflict graph). We study fundamental properties of a spatial congestion game; in particular, we seek to answer under what conditions this game possesses the finite improvement property or a Nash equilibrium. We also discuss the implications of these results when applied to wireless spectrum sharing.

Connected through hyperedge HE-1 (induced by source paper "coordinating the crowd inducing desirable equilibria in non cooperative systems"):
  with members:
    - "decentralised learning in systems with many many strategic agents"
    - "learning parametric closed loop policies for markov potential games"
    - "balancing two player stochastic games with soft q learning"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "dynamic potential games in communications fundamentals and applications"

Connected through hyperedge HE-2 (induced by source paper "decentralised learning in systems with many many strategic agents"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "multiagent learning in large anonymous games"

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)