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: "training gans with optimism"
  Abstract: We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics. Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs. We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle. We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum. We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences. We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants. Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam. We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam.

Connected through hyperedge HE-1 (induced by source paper "gans may have no nash equilibria"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "lipschitz generative adversarial nets"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"

Connected through hyperedge HE-2 (induced by source paper "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "bandit learning in concave n person games"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a generic approach for escaping saddle points"
    - "cycles in adversarial regularized learning"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"

Connected through hyperedge HE-3 (induced by source paper "flexible numerical optimization with ensmallen"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "practical sparse matrices in c with hybrid storage and template based expression optimisation"
    - "a user friendly hybrid sparse matrix class in c"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "ensmallen a flexible c library for efficient function optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-4 (induced by source paper "optimality and stability in non convex non concave min max optimization"):
  with members:
    - "training gans with centripetal acceleration"
    - "convergence behaviour of some gradient based methods on bilinear games"
    - "lower bounds and conditioning of differentiable games"
    - "linear lower bounds and conditioning of differentiable games"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-5 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems"):
  with members:
    - "accelerating smooth games by manipulating spectral shapes"
    - "a tight and unified analysis of extragradient for a whole spectrum of differentiable games"
    - "training gans with centripetal acceleration"
    - "lower bounds and conditioning of differentiable games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "a variational inequality perspective on generative adversarial nets"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-6 (induced by source paper "a unified analysis of gradient based methods for a whole spectrum of games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-7 (induced by source paper "distributed no regret learning in multi agent systems"):
  with members:
    - "policy regret in repeated games"
    - "negative momentum for improved game dynamics"
    - "bandit learning in concave n person games"
    - "learning in time varying games"
    - "cycles in adversarial regularized learning"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "delay and cooperation in nonstochastic bandits"
    - "learning and efficiency in games with dynamic population"

Connected through hyperedge HE-8 (induced by source paper "local nash equilibria are isolated strict local nash equilibria in almost all zero sum continuous games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "robust adversarial reinforcement learning"
    - "cycles in adversarial regularized learning"

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.LG(Machine Learning)