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: "the price of anarchy in large games"
  Abstract: Game-theoretic models relevant for computer science applications usually feature a large number of players. The goal of this paper is to develop an analytical framework for bounding the price of anarchy in such models. We demonstrate the wide applicability of our framework through instantiations for several well-studied models, including simultaneous single-item auctions, greedy combinatorial auctions, and routing games. In all cases, we identify conditions under which the POA of large games is much better than that of worst-case instances. Our results also give new senses in which simple auctions can perform almost as well as optimal ones in realistic settings.

Connected through hyperedge HE-1 (induced by source paper "convergence of large atomic congestion games"):
  with members:
    - "routing game on parallel networks the convergence of atomic to nonatomic"
    - "price of anarchy for non atomic congestion games with stochastic demands"

Connected through hyperedge HE-2 (induced by source paper "envy freeness up to any item with high nash welfare the virtue of donating items"):
  with members:
    - "comparing approximate relaxations of envy freeness"
    - "on fair division of indivisible items"
    - "approximation algorithms for computing maximin share allocations"
    - "almost envy freeness with general valuations"
    - "approximating the nash social welfare with budget additive valuations"
    - "finding fair and efficient allocations"
    - "a discrete and bounded envy free cake cutting protocol for any number of agents"
    - "convex program duality fisher markets and nash social welfare"

Connected through hyperedge HE-3 (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-4 (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"
    - "on the price of anarchy of highly congested nonatomic network games"

Connected through hyperedge HE-5 (induced by source paper "the price of governance a middle ground solution to coordination in organizational control"):
  with members:
    - "an optimal rewiring strategy for reinforcement social learning in cooperative multiagent systems"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "how bad is selfish routing in practice"):
  with members:
    - "power saving transportation mode identification for large scale applications"
    - "data driven estimation of origin destination demand and user cost functions for the optimization of transportation networks"
    - "robust data driven guarantees in auctions"
    - "econometrics for learning agents"

Connected through hyperedge HE-8 (induced by source paper "the price of anarchy in auctions"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "simple mechanisms for agents with complements"
    - "on the economic efficiency of the combinatorial clock auction"
    - "on the efficiency of all pay mechanisms"
    - "learning and efficiency in games with dynamic population"
    - "no regret learning in bayesian games"
    - "communication complexity for algorithm designers"

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)