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: "approximating nash equilibria for black box games a bayesian optimization approach"
  Abstract: Game theory has emerged as a powerful framework for modeling a large range of multi-agent scenarios. Many algorithmic solutions require discrete, finite games with payoffs that have a closed-form specification. In contrast, many real-world applications require modeling with continuous action spaces and black-box utility functions where payoff information is available only in the form of empirical (often expensive and/or noisy) observations of strategy profiles. To the best of our knowledge, few tools exist for solving the class of expensive, black-box continuous games. In this paper, we develop a method to find equilibria for such games in a sequential decision-making framework using Bayesian Optimization. The proposed approach is validated on a collection of synthetic game problems with varying degree of noise and action space dimensions. The results indicate that it is capable of improving the maximum regret in noisy and high dimensions to a greater extent than hierarchical or discretized methods.

Connected through hyperedge HE-1 (induced by source paper "min max optimization without gradients convergence and applications to adversarial ml"):
  with members:
    - "beyond adversarial training min max optimization in adversarial attack and defense"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "robust optimization over multiple domains"
    - "spectral signatures in backdoor attacks"
    - "caad 2018 iterative ensemble adversarial attack"
    - "towards distributed coevolutionary gans"
    - "certified defenses for data poisoning attacks"

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)