human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "optimality and stability in non convex non concave min max optimization"):
  Abstract: Convergence to a saddle point for convex-concave functions has been studied for decades, while the last few years have seen a surge of interest in non-convex-non-concave min-max optimization due to the rise of deep learning. However, it remains an intriguing research challenge how local optimal points are defined and which algorithm can converge to such points. We study definitions of "local min-max (max-min)" points and provide an elegant unification, with the corresponding first- and second-order necessary and sufficient conditions. Specifically, we show that quadratic games, as often used as illustrative examples and approximations of smooth functions, are too special, both locally and globally. Lastly, we analyze the exact conditions for local convergence of several popular gradient algorithms near the "local min-max" points defined in the previous section, identify "valid" hyper-parameters and compare the respective stable sets. Our results offer insights into the necessity of two-time-scale algorithms and the limitation of the commonly used approach based on ordinary differential equations.

Includes members:
  Member "training gans with centripetal acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems")
  Member "convergence behaviour of some gradient based methods on bilinear games":
    also connected through other hyperedges:
      (none)
  Member "lower bounds and conditioning of differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems")
  Member "linear lower bounds and conditioning of differentiable games":
    also connected through other hyperedges:
      (none)
  Member "on solving minimax optimization locally a follow the ridge approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gans may have no nash equilibria")
  Member "convergence of learning dynamics in stackelberg games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gans may have no nash equilibria")
  Member "optimistic mirror descent in saddle point problems going the extra gradient mile":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems")
  Member "sbeed convergent reinforcement learning with nonlinear function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")

Question: Please tell me which class the source paper 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)