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: "convergence to equilibrium of logit dynamics for strategic games"
  Abstract: We present the first general bounds on the mixing time of the Markov chain associated to the logit dynamics for wide classes of strategic games. The logit dynamics with inverse noise beta describes the behavior of a complex system whose individual components act selfishly and keep responding according to some partial ("noisy") knowledge of the system, where the capacity of the agent to know the system and compute her best move is measured by the inverse of the parameter beta. #R##N#In particular, we prove nearly tight bounds for potential games and games with dominant strategies. Our results show that, for potential games, the mixing time is upper and lower bounded by an exponential in the inverse of the noise and in the maximum potential difference. Instead, for games with dominant strategies, the mixing time cannot grow arbitrarily with the inverse of the noise. #R##N#Finally, we refine our analysis for a subclass of potential games called graphical coordination games, a class of games that have been previously studied in Physics and, more recently, in Computer Science in the context of diffusion of new technologies. We give evidence that the mixing time of the logit dynamics for these games strongly depends on the structure of the underlying graph. We prove that the mixing time of the logit dynamics for these games can be upper bounded by a function that is exponential in the cutwidth of the underlying graph and in the inverse of noise. Moreover, we consider two specific and popular network topologies, the clique and the ring. For games played on a clique we prove an almost matching lower bound on the mixing time of the logit dynamics that is exponential in the inverse of the noise and in the maximum potential difference, while for games played on a ring we prove that the time of convergence of the logit dynamics to its stationary distribution is significantly shorter.

Connected through hyperedge HE-1 (induced by source paper "path to stochastic stability comparative analysis of stochastic learning dynamics in games"):
  with members:
    - "metastability of logit dynamics for coordination games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-2 (induced by source paper "generalized mirror descents in congestion games"):
  with members:
    - "learning equilibria of games via payoff queries"
    - "concurrent imitation dynamics in congestion games"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-3 (induced by source paper "price competition fluctuations and welfare guarantees"):
  with members:
    - "price competition in online combinatorial markets"
    - "logit dynamics with concurrent updates for local interaction games"
    - "composable and efficient mechanisms"
    - "metastability of potential games"
    - "metastability of logit dynamics for coordination games"
    - "mixing time and stationary expected social welfare of logit dynamics"

Connected through hyperedge HE-4 (induced by source paper "decentralized dynamics for finite opinion games"):
  with members:
    - "minority becomes majority in social networks"
    - "on discrete preferences and coordination"
    - "how bad is forming your own opinion"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "metastability of logit dynamics for coordination games"
    - "mixing time and stationary expected social welfare of logit dynamics"

Connected through hyperedge HE-5 (induced by source paper "logit dynamics with concurrent updates for local interaction games"):
  with members:
    - "decentralized dynamics for finite opinion games"
    - "how bad is forming your own opinion"
    - "metastability of logit dynamics for coordination games"
    - "the hitchhiker s guide to affiliation networks a game theoretic approach"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-6 (induced by source paper "metastability of asymptotically well behaved potential games"):
  with members:
    - "decentralized dynamics for finite opinion games"
    - "metastability of potential games"
    - "metastability of logit dynamics for coordination games"
    - "mixing time and stationary expected social welfare of logit dynamics"

Connected through hyperedge HE-7 (induced by source paper "metastability of potential games"):
  with members:
    - "efficient nash computation in large population games with bounded influence"
    - "metastability of logit dynamics for coordination games"
    - "mixing time and stationary expected social welfare of logit dynamics"
    - "computing nash equilibria approximation and smoothed complexity"

Connected through hyperedge HE-8 (induced by source paper "imperfect best response mechanisms"):
  with members:
    - "logit dynamics with concurrent updates for local interaction games"
    - "mixing time and stationary expected social welfare of logit dynamics"

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)