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: "finding any nontrivial coarse correlated equilibrium is hard"
  Abstract: One of the most appealing aspects of the (coarse) correlated equilibrium concept is that natural dynamics quickly arrive at approximations of such equilibria, even in games with many players. In addition, there exist polynomial-time algorithms that compute exact (coarse) correlated equilibria. In light of these results, a natural question is how good are the (coarse) correlated equilibria that can arise from any efficient algorithm or dynamics. #R##N#In this paper we address this question, and establish strong negative results. In particular, we show that in multiplayer games that have a succinct representation, it is NP-hard to compute any coarse correlated equilibrium (or approximate coarse correlated equilibrium) with welfare strictly better than the worst possible. The focus on succinct games ensures that the underlying complexity question is interesting; many multiplayer games of interest are in fact succinct. Our results imply that, while one can efficiently compute a coarse correlated equilibrium, one cannot provide any nontrivial welfare guarantee for the resulting equilibrium, unless P=NP. We show that analogous hardness results hold for correlated equilibria, and persist under the egalitarian objective or Pareto optimality. #R##N#To complement the hardness results, we develop an algorithmic framework that identifies settings in which we can efficiently compute an approximate correlated equilibrium with near-optimal welfare. We use this framework to develop an efficient algorithm for computing an approximate correlated equilibrium with near-optimal welfare in aggregative games.

Connected through hyperedge HE-1 (induced by source paper "no regret learning in cournot games"):
  with members:
    - "introduction to online convex optimization"
    - "finding friend and foe in multi agent games"
    - "bandit learning in concave n person games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "learning in games robustness of fast convergence"
    - "fast convergence of regularized learning in games"
    - "on the role of a market maker in networked cournot competition"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-2 (induced by source paper "signaling in bayesian network congestion games the subtle power of symmetry"):
  with members:
    - "bayesian persuasion with sequential games"
    - "on the tractability of public persuasion with no externalities"
    - "value of information systems in routing games"
    - "informational braess paradox the effect of information on traffic congestion"
    - "a general framework for computing optimal correlated equilibria in compact games"

Connected through hyperedge HE-3 (induced by source paper "learning to correlate in multi player general sum sequential games"):
  with members:
    - "computing optimal coarse correlated equilibria in sequential games"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "no regret learning in bayesian games"

Connected through hyperedge HE-4 (induced by source paper "computing constrained approximate equilibria in polymatrix games"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "fptas for mixed strategy nash equilibria in tree graphical games and their generalizations"
    - "approximating nash equilibria in tree polymatrix games"
    - "an empirical study on computing equilibria in polymatrix games"
    - "inapproximability results for approximate nash equilibria"
    - "inapproximability of nash equilibrium"
    - "computing approximate nash equilibria in polymatrix games"
    - "approximate well supported nash equilibria below two thirds"

Connected through hyperedge HE-5 (induced by source paper "communication complexity of correlated equilibrium in two player games"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity for algorithm designers"
    - "inapproximability of nash equilibrium"
    - "on the communication complexity of approximate nash equilibria"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "robust mediators in large games"):
  with members:
    - "privacy and truthful equilibrium selection for aggregative games"
    - "differential privacy for the analyst via private equilibrium computation"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-7 (induced by source paper "privacy and truthful equilibrium selection for aggregative games"):
  with members:
    - "private matchings and allocations"
    - "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"
    - "calibrating data to sensitivity in private data analysis"

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)