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: "a variational inequality perspective on generative adversarial nets"
  Abstract: Stability has been a recurrent issue in training generative adversarial networks (GANs). One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods specifically designed for this adversarial training. In this work, we review the "variational inequality" framework which contains most formulations of the GAN objective introduced so far. Taping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend standard methods designed for variational inequalities to GANs training, such as a stochastic version of the extragradient method, and empirically investigate their behavior on GANs.

Connected through hyperedge HE-1 (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"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "negative momentum for improved game dynamics"
    - "the numerics of gans"

Connected through hyperedge HE-2 (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"
    - "the mechanics of n player differentiable games"
    - "robust adversarial reinforcement learning"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"

Connected through hyperedge HE-3 (induced by source paper "accelerating smooth games by manipulating spectral shapes"):
  with members:
    - "a tight and unified analysis of extragradient for a whole spectrum of differentiable games"
    - "differentiable game mechanics"
    - "the mechanics of n player differentiable games"
    - "the numerics of gans"
    - "training gans with optimism"
    - "stochastic variance reduction methods for saddle point problems"

Connected through hyperedge HE-4 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"
    - "online learning rate adaptation with hypergradient descent"

Connected through hyperedge HE-5 (induced by source paper "a closer look at the optimization landscapes of generative adversarial networks"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-6 (induced by source paper "differentiable game mechanics"):
  with members:
    - "re evaluating evaluation"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "strongly typed agents are guaranteed to interact safely"
    - "learning with opponent learning awareness"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "ode analysis of stochastic gradient methods with optimism and anchoring for minimax problems and gans"):
  with members:
    - "training gans with centripetal acceleration"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "the numerics of gans"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-8 (induced by source paper "stein bridging enabling mutual reinforcement between explicit and implicit generative models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "learning implicit generative models by teaching explicit ones"
    - "large scale gan training for high fidelity natural image synthesis"
    - "calibrating energy based generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "wasserstein distributional robustness and regularization in statistical 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)