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 "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.

Includes members:
  Member "a variational inequality perspective on generative adversarial nets":
    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 "eigenvalues of the hessian in deep learning singularity and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited")
  Member "neon2 finding local minima via first order oracles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points")
  Member "the numerics of gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "gradient descent gan optimization is locally stable":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial code learning for image generation")
  Member "identifying and attacking the saddle point problem in high dimensional non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")

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)