human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "what is local optimality in nonconvex nonconcave minimax optimization"):
  Abstract: Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises---``what is a proper definition of local optima?'' #R##N#Most previous work answers this question using classical notions of equilibria from simultaneous games, where the min-player and the max-player act simultaneously. In contrast, most applications in machine learning, including GANs and adversarial training, correspond to sequential games, where the order of which player acts first is crucial (since minimax is in general not equal to maximin due to the nonconvex-nonconcave nature of the problems). The main contribution of this paper is to propose a proper mathematical definition of local optimality for this sequential setting---local minimax, as well as to present its properties and existence results. Finally, we establish a strong connection to a basic local search algorithm---gradient descent ascent (GDA): under mild conditions, all stable limit points of GDA are exactly local minimax points up to some degenerate points.

Includes members:
  Member "on gradient based learning in continuous games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "on finding local nash equilibria and only local nash equilibria in zero sum games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gans may have no nash equilibria")
  Member "local saddle point optimization a curvature exploitation approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a mean field analysis of two player zero sum games")
  Member "on the convergence of gradient based learning in continuous games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "learning in non convex games with an optimization oracle":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "correlated adversarial imitation learning")
  Member "negative momentum for improved game dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "finding mixed nash equilibria of generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gans may have no nash equilibria")

Question: Please tell me which class the source paper 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.LG(Machine Learning)