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: "concurrent imitation dynamics in congestion games"
  Abstract: Imitating successful behavior is a natural and frequently applied approach to trust in when facing scenarios for which we have little or no experience upon which we can base our decision. In this paper, we consider such behavior in atomic congestion games. We propose to study concurrent imitation dynamics that emerge when each player samples another player and possibly imitates this agents' strategy if the anticipated latency gain is sufficiently large. Our main focus is on convergence properties. Using a potential function argument, we show that our dynamics converge in a monotonic fashion to stable states. In such a state none of the players can improve its latency by imitating somebody else. As our main result, we show rapid convergence to approximate equilibria. At an approximate equilibrium only a small fraction of agents sustains a latency significantly above or below average. In particular, imitation dynamics behave like fully polynomial time approximation schemes (FPTAS). Fixing all other parameters, the convergence time depends only in a logarithmic fashion on the number of agents. Since imitation processes are not innovative they cannot discover unused strategies. Furthermore, strategies may become extinct with non-zero probability. For the case of singleton games, we show that the probability of this event occurring is negligible. Additionally, we prove that the social cost of a stable state reached by our dynamics is not much worse than an optimal state in singleton congestion games with linear latency function. Finally, we discuss how the protocol can be extended such that, in the long run, dynamics converge to a Nash equilibrium.

Connected through hyperedge HE-1 (induced by source paper "routing games in the wild efficiency equilibration and regret large scale field experiments in singapore"):
  with members:
    - "power saving transportation mode identification for large scale applications"
    - "learning and trust in auction markets"
    - "data driven estimation of origin destination demand and user cost functions for the optimization of transportation networks"
    - "on the price of anarchy of highly congested nonatomic network games"
    - "the price of anarchy in large games"
    - "robust data driven guarantees in auctions"
    - "learning in auctions regret is hard envy is easy"
    - "econometrics for learning agents"

Connected through hyperedge HE-2 (induced by source paper "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"):
  with members:
    - "evolutionary stability implies asymptotic stability under multiplicative weights"
    - "multiplicative updates in coordination games and the theory of evolution"
    - "efficient computation of approximate pure nash equilibria in congestion games"

Connected through hyperedge HE-3 (induced by source paper "generalized mirror descents in congestion games"):
  with members:
    - "learning equilibria of games via payoff queries"
    - "convergence to equilibrium of logit dynamics for strategic games"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "the price of anarchy and stability in general noisy best response dynamics"):
  with members:
    - "multicast network design game on a ring"
    - "tighter bounds on the inefficiency ratio of stable equilibria in load balancing games"
    - "logit dynamics with concurrent updates for local interaction games"
    - "on the impact of fair best response dynamics"
    - "mixing time and stationary expected social welfare of logit dynamics"
    - "on the performance of approximate equilibria in congestion games"

Connected through hyperedge HE-5 (induced by source paper "approximating the geometry of dynamics in potential games point wise convergence regions of attraction average case performance analysis and system invariants"):
  with members:
    - "multiplicative updates in coordination games and the theory of evolution"

Connected through hyperedge HE-6 (induced by source paper "from pointwise convergence of evolutionary dynamics to average case analysis of decentralized algorithms"):
  with members:
    - "multiplicative updates in coordination games and the theory of evolution"

Connected through hyperedge HE-7 (induced by source paper "average case performance of replicator dynamics in potential games via computing regions of attraction"):
  with members:
    - "distributed selfish load balancing"

Connected through hyperedge HE-8 (induced by source paper "convergence to equilibrium of logit dynamics for strategic games"):
  with members:
    - "logit dynamics with concurrent updates for local interaction games"
    - "metastability of logit dynamics for coordination 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)