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: "efficient nash computation in large population games with bounded influence"
  Abstract: We introduce a general representation of large-population games in which each player s influence ON the others IS centralized AND limited, but may otherwise be arbitrary.This representation significantly generalizes the class known AS congestion games IN a natural way.Our main results are provably correct AND efficient algorithms FOR computing AND learning approximate Nash equilibria IN this general framework.

Connected through hyperedge HE-1 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "optimal auctions for correlated buyers with sampling"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "revenue optimization in posted price auctions with strategic buyers"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-2 (induced by source paper "strategic prediction with latent aggregative games"):
  with members:
    - "the mechanics of n player differentiable games"
    - "learning to speed up structured output prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "learning with opponent learning awareness"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "on structured prediction theory with calibrated convex surrogate losses"

Connected through hyperedge HE-3 (induced by source paper "privacy and truthful equilibrium selection for aggregative games"):
  with members:
    - "private matchings and allocations"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "privately solving linear programs"
    - "on the complexity of nash equilibria in anonymous games"
    - "privacy preserving public information for sequential games"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "privacy and mechanism design"

Connected through hyperedge HE-4 (induced by source paper "metastability of potential games"):
  with members:
    - "convergence to equilibrium of logit dynamics for strategic games"
    - "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-5 (induced by source paper "bounding the uncertainty of graphical games the complexity of simple requirements pareto and strong nash equilibria"):
  with members:
    - "graphical models for game theory"
    - "pure nash equilibria hard and easy games"
    - "complexity results about nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "learning and solving many player games through a cluster based representation"):
  with members:
    - "graphical models for game theory"
    - "computing nash equilibria of action graph games"
    - "computing equilibria in anonymous games"

Connected through hyperedge HE-7 (induced by source paper "computing nash equilibria of action graph games"):
  with members:
    - "graphical models for game theory"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-8 (induced by source paper "the complexity of approximate nash equilibrium in congestion games with negative delays"):
  with members:
    - "computing nash equilibria approximation and smoothed complexity"

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)