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: "on the price of anarchy of highly congested nonatomic network games"
  Abstract: We consider nonatomic network games with one source and one destination. We examine the asymptotic behavior of the price of anarchy as the inflow increases. In accordance with some empirical observations, we show that, under suitable conditions, the price of anarchy is asymptotic to one. We show with some counterexamples that this is not always the case. The counterexamples occur in simple parallel graphs.

Connected through hyperedge HE-1 (induced by source paper "the route to chaos in routing games when is price of anarchy too optimistic"):
  with members:
    - "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"
    - "alpha rank multi agent evaluation by evolution"
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "multi agent learning in network zero sum games is a hamiltonian system"
    - "learning dynamics and the co evolution of competing sexual species"
    - "shrewd selection speeds surfing use smart exp3"
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"

Connected through hyperedge HE-2 (induced by source paper "the route to chaos in routing games population increase drives period doubling instability chaos inefficiency with price of anarchy equal to one"):
  with members:
    - "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"
    - "alpha rank multi agent evaluation by evolution"
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "cooperation speeds surfing use co bandit"
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "learning in games robustness of fast convergence"
    - "the price of anarchy in large games"

Connected through hyperedge HE-3 (induced by source paper "the price of anarchy in routing games as a function of the demand"):
  with members:
    - "demand independent optimal tolls"
    - "wealth inequality and the price of anarchy"

Connected through hyperedge HE-4 (induced by source paper "selfishness need not be bad a general proof"):
  with members:
    - "selfishness need not be bad"
    - "when is selfish routing bad the price of anarchy in light and heavy traffic"

Connected through hyperedge HE-5 (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"
    - "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"
    - "playing the wrong game smoothness bounds for congestion games with behavioral biases"

Connected through hyperedge HE-6 (induced by source paper "when is selfish routing bad the price of anarchy in light and heavy traffic"):
  with members:
    - "how bad is selfish routing in practice"
    - "selfishness need not be bad"

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)