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: "dynamics in near potential games"
  Abstract: Except for special classes of games, there is no systematic framework for analyzing the dynamical properties of multi-agent strategic interactions. Potential games are one such special but restrictive class of games that allow for tractable dynamic analysis. Intuitively, games that are "close" to a potential game should share similar properties. In this paper, we formalize and develop this idea by quantifying to what extent the dynamic features of potential games extend to "near-potential" games. We study convergence of three commonly studied classes of adaptive dynamics: discrete-time better/best response, logit response, and discrete-time fictitious play dynamics. For better/best response dynamics, we focus on the evolution of the sequence of pure strategy profiles and show that this sequence converges to a (pure) approximate equilibrium set, whose size is a function of the "distance" from a close potential game. We then study logit response dynamics and provide a characterization of the stationary distribution of this update rule in terms of the distance of the game from a close potential game and the corresponding potential function. We further show that the stochastically stable strategy profiles are pure approximate equilibria. Finally, we turn attention to fictitious play, and establish that the sequence of empirical frequencies of player actions converges to a neighborhood of (mixed) equilibria of the game, where the size of the neighborhood increases with distance of the game to a potential game. Thus, our results suggest that games that are close to a potential game inherit the dynamical properties of potential games. Since a close potential game to a given game can be found by solving a convex optimization problem, our approach also provides a systematic framework for studying convergence behavior of adaptive learning dynamics in arbitrary finite strategic form games.

Connected through hyperedge HE-1 (induced by source paper "approximate best response dynamics in random interference games"):
  with members:
    - "asymptotically optimal distributed channel allocation a competitive game theoretic approach"
    - "distributed learning algorithms for spectrum sharing in spatial random access wireless networks"
    - "on the structure of weakly acyclic games"
    - "game theory and the frequency selective interference channel"
    - "on the complexity of nash dynamics and sink equilibria"
    - "cooperative game theory and the gaussian interference channel"

Connected through hyperedge HE-2 (induced by source paper "are multiagent systems resilient to communication failures"):
  with members:
    - "fast convergence in semi anonymous potential games"
    - "potential games are necessary to ensure pure nash equilibria in cost sharing games"
    - "using collective intelligence to route internet traffic"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive survey of potential game approaches to wireless networks"):
  with members:
    - "centralized distributed spectrum access for small cell networks a cloud based game solution"
    - "decentralized computation offloading game for mobile cloud computing"
    - "database assisted spectrum access in dynamic networks a distributed learning solution"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "distributed learning algorithms for spectrum sharing in spatial random access wireless networks"
    - "spatial spectrum access game"
    - "joint access point selection and power allocation for uplink wireless networks"
    - "database assisted distributed spectrum sharing"

Connected through hyperedge HE-4 (induced by source paper "hodge laplacians on graphs"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "graph isomorphism in quasipolynomial time"
    - "algorithm 937 minres qlp for symmetric and hermitian linear equations and least squares problems"
    - "flows and decompositions of games harmonic and potential games"
    - "rank aggregation via nuclear norm minimization"
    - "least squares ranking on graphs"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"

Connected through hyperedge HE-5 (induced by source paper "approximate pure nash equilibria in weighted congestion games existence efficient computation and structure"):
  with members:
    - "efficient coordination mechanisms for unrelated machine scheduling"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "computing equilibria in anonymous games"
    - "near rationality and competitive equilibria in networked systems"

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)