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: "local saddle point optimization a curvature exploitation approach"
  Abstract: Gradient-based optimization methods are the most popular choice for finding local optima for classical minimization and saddle point problems. Here, we highlight a systemic issue of gradient dynamics that arise for saddle point problems, namely the presence of undesired stable stationary points that are no local optima. We propose a novel optimization approach that exploits curvature information in order to escape from these undesired stationary points. We prove that different optimization methods, including gradient method and adagrad, equipped with curvature exploitation can escape non-optimal stationary points. We also provide empirical results on common saddle point problems which confirm the advantage of using curvature exploitation.

Connected through hyperedge HE-1 (induced by source paper "a mean field analysis of two player zero sum games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "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-2 (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"
    - "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-3 (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"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on solving minimax optimization locally a follow the ridge approach"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "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"
    - "convergence of learning dynamics in stackelberg games"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "global convergence to the equilibrium of gans using variational inequalities"

Connected through hyperedge HE-6 (induced by source paper "what is local optimality in nonconvex nonconcave minimax optimization"):
  with members:
    - "on gradient based learning in continuous games"
    - "introduction to online convex optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "on the convergence of gradient based learning in continuous games"
    - "learning in non convex games with an optimization oracle"
    - "negative momentum for improved game dynamics"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-7 (induced by source paper "on gradient descent ascent for nonconvex concave minimax problems"):
  with members:
    - "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"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "solving approximate wasserstein gans to stationarity"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "an online learning approach to generative adversarial networks"
    - "the numerics of gans"

Connected through hyperedge HE-8 (induced by source paper "a tight and unified analysis of extragradient for a whole spectrum of differentiable 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"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "the numerics of gans"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"

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)