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 "learning in non convex games with an optimization oracle"):
  Abstract: We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially more computation than statistical learning. In this paper we show that by slightly strengthening the oracle model, the online and the statistical learning models become computationally equivalent. Our result holds for any Lipschitz and bounded (but not necessarily convex) function. As an application we demonstrate how the offline oracle enables efficient computation of an equilibrium in non-convex games, that include GAN (generative adversarial networks) as a special case.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "on convergence and stability of gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "efficient regret minimization in non convex games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rnn based online learning an efficient first order optimization algorithm with a convergence guarantee")
  Member "fast rates for empirical risk minimization of strict saddle problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "oracle efficient online learning and auction design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private learning implies online learning an efficient reduction")
  Member "the computational power of optimization in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "taking a hint how to leverage loss predictors in contextual bandits")
  Member "prediction by random walk perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minimax regret of switching constrained online convex optimization no phase transition")
  Member "on the generalization ability of online learning algorithms for pairwise loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stability and optimization error of stochastic gradient descent for pairwise learning")

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)