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: "optimistic mirror descent in saddle point problems going the extra gradient mile"
  Abstract: Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necessity, most theoretical guarantees revolve around convex-concave (or even linear) problems; however, making theoretical inroads towards efficient GAN training depends crucially on moving beyond this classic framework. To make piecemeal progress along these lines, we analyze the behavior of mirror descent (MD) in a class of non-monotone problems whose solutions coincide with those of a naturally associated variational inequality - a property which we call coherence. We first show that ordinary, "vanilla" MD converges under a strict version of this condition, but not otherwise; in particular, it may fail to converge even in bilinear models with a unique solution. We then show that this deficiency is mitigated by optimism: by taking an "extra-gradient" step, optimistic mirror descent (OMD) converges in all coherent problems. Our analysis generalizes and extends the results of Daskalakis et al. (2018) for optimistic gradient descent (OGD) in bilinear problems, and makes concrete headway for establishing convergence beyond convex-concave games. We also provide stochastic analogues of these results, and we validate our analysis by numerical experiments in a wide array of GAN models (including Gaussian mixture models, as well as the CelebA and CIFAR-10 datasets).

Connected through hyperedge HE-1 (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"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "the numerics of gans"

Connected through hyperedge HE-2 (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"
    - "negative momentum for improved game dynamics"
    - "the numerics of gans"

Connected through hyperedge HE-3 (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"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "deep clustering for unsupervised learning of visual features"
    - "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:
    - "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"
    - "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 "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"
    - "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"

Connected through hyperedge HE-6 (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"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "global convergence to the equilibrium of gans using variational inequalities"

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"
    - "a variational inequality perspective on generative adversarial nets"
    - "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 "convergence of learning dynamics in stackelberg games"):
  with members:
    - "on gradient based learning in continuous games"
    - "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"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "goal inference improves objective and perceived performance in human robot collaboration"

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)