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: "on finding local nash equilibria and only local nash equilibria in zero sum games"
  Abstract: We propose local symplectic surgery, a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms cannot guarantee convergence to local Nash equilibria due to the existence of non-Nash stationary points. By taking advantage of the differential structure of the game, we construct an algorithm for which the local Nash equilibria are the only attracting fixed points. We also show that the algorithm exhibits no oscillatory behaviors in neighborhoods of equilibria and show that it has the same per-iteration complexity as other recently proposed algorithms. We conclude by validating the algorithm on two numerical examples: a toy example with multiple Nash equilibria and a non-Nash equilibrium, and the training of a small generative adversarial network (GAN).

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"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"
    - "a convex duality framework for gans"

Connected through hyperedge HE-2 (induced by source paper "inference based strategy alignment for general sum differential games"):
  with members:
    - "on gradient based learning in continuous games"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "on the convergence of gradient based learning in continuous games"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "multimodal probabilistic model based planning for human robot interaction"

Connected through hyperedge HE-3 (induced by source paper "a mean field analysis of two player zero sum games"):
  with members:
    - "sgd learns one layer networks in wgans"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "deep clustering for unsupervised learning of visual features"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "multi agent diverse generative adversarial networks"
    - "an online learning approach to generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a unified analysis of gradient based methods for a whole spectrum of games"):
  with members:
    - "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"
    - "training gans with optimism"

Connected through hyperedge HE-5 (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"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "robust adversarial reinforcement learning"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"

Connected through hyperedge HE-6 (induced by source paper "implicit competitive regularization in gans"):
  with members:
    - "coherent gradients an approach to understanding generalization in gradient descent based optimization"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "a gradual semi discrete approach to generative network training via explicit wasserstein minimization"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "implicit regularization in deep matrix factorization"
    - "implicit regularization in over parameterized neural networks"
    - "convergence of learning dynamics in stackelberg games"

Connected through hyperedge HE-7 (induced by source paper "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"):
  with members:
    - "on gradient based learning in continuous games"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "a theoretical analysis of deep q learning"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "local saddle point optimization a curvature exploitation approach"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"

Connected through hyperedge HE-8 (induced by source paper "a closer look at the optimization landscapes of generative adversarial networks"):
  with members:
    - "negative eigenvalues of the hessian in deep neural networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "a variational inequality perspective on generative adversarial nets"
    - "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"

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)