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 centripetal acceleration"
  Abstract: Training generative adversarial networks (GANs) often suffers from cyclic behaviors of iterates. Based on a simple intuition that the direction of centripetal acceleration of an object moving in uniform circular motion is toward the center of the circle, we present the Simultaneous Centripetal Acceleration (SCA) method and the Alternating Centripetal Acceleration (ACA) method to alleviate the cyclic behaviors. Under suitable conditions, gradient descent methods with either SCA or ACA are shown to be linearly convergent for bilinear games. Numerical experiments are conducted by applying ACA to existing gradient-based algorithms in a GAN setup scenario, which demonstrate the superiority of ACA.

Connected through hyperedge HE-1 (induced by source paper "optimality and stability in non convex non concave min max optimization"):
  with members:
    - "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"
    - "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"
    - "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"
    - "the numerics of gans"

Connected through hyperedge HE-3 (induced by source paper "ode analysis of stochastic gradient methods with optimism and anchoring for minimax problems and gans"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "a variational inequality perspective on generative adversarial nets"
    - "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-4 (induced by source paper "understanding and stabilizing gans training dynamics with control theory"):
  with members:
    - "large scale adversarial representation learning"
    - "learning implicit generative models by teaching explicit ones"
    - "large scale gan training for high fidelity natural image synthesis"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "dual discriminator generative adversarial nets"
    - "the numerics of gans"
    - "triple generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "convergence of gradient methods on bilinear zero sum games"):
  with members:
    - "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"
    - "which training methods for gans do actually converge"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "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)