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 "game theoretic optimization via gradient based nikaido isoda function"):
  Abstract: Computing Nash equilibrium (NE) of multi-player games has witnessed renewed interest due to recent advances in generative adversarial networks. However, computing equilibrium efficiently is challenging. To this end, we introduce the Gradient-based Nikaido-Isoda (GNI) function which serves: (i) as a merit function, vanishing only at the first-order stationary points of each player's optimization problem, and (ii) provides error bounds to a stationary Nash point. Gradient descent is shown to converge sublinearly to a first-order stationary point of the GNI function. For the particular case of bilinear min-max games and multi-player quadratic games, the GNI function is convex. Hence, the application of gradient descent in this case yields linear convergence to an NE (when one exists). In our numerical experiments, we observe that the GNI formulation always converges to the first-order stationary point of each player's optimization problem.

Includes members:
  Member "a variational inequality perspective on generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems")
  Member "optimistic mirror descent in saddle point problems going the extra gradient mile":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimality and stability in non convex non concave min max optimization")
  Member "learning with minimal information in continuous games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bandit learning in concave n person games")
  Member "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial code learning for image generation")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "stabilizing adversarial nets with prediction methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems")
  Member "gradient descent gan optimization is locally stable":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial code learning for image generation")

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)