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: "convergence behaviour of some gradient based methods on bilinear games"
  Abstract: Min-max optimization has attracted much attention in the machine learning community due to the popularization of deep generative models and adversarial training. The optimization is quite different from traditional minimization analysis. For example, gradient descent does not converge in one of the simplest settings -- bilinear games. In this paper, we try to understand several gradient-based algorithms for bilinear min-max games: gradient descent, extra-gradient, optimistic gradient descent and the momentum method, for both simultaneous and alternating updates. We provide necessary and sufficient conditions for their convergence, with the Schur theorem. Furthermore, by extending these algorithms to more general parameter settings, we are able to optimize over larger parameter spaces to find the optimal convergence rates. Our results imply that alternating updates converge more easily in min-max games than simultaneous updates.

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"
    - "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"

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)