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: "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.

Connected through hyperedge HE-1 (induced by source paper "correlated adversarial imitation learning"):
  with members:
    - "causal confusion in imitation learning"
    - "maximum causal tsallis entropy imitation learning"
    - "quantifying generalization in reinforcement learning"
    - "on convergence and stability of gans"
    - "an online learning approach to generative adversarial networks"
    - "efficient regret minimization in non convex games"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "convergence of adversarial training in overparametrized neural networks"):
  with members:
    - "a gram gauss newton method learning overparameterized deep neural networks for regression problems"
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "adversarial training for free"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-3 (induced by source paper "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "how to use heuristics for differential privacy"
    - "calibrating noise to variance in adaptive data analysis"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-4 (induced by source paper "what is local optimality in nonconvex nonconcave minimax optimization"):
  with members:
    - "on gradient based learning in continuous games"
    - "introduction to online convex optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "local saddle point optimization a curvature exploitation approach"
    - "on the convergence of gradient based learning in continuous games"
    - "negative momentum for improved game dynamics"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "dual averaging method for online graph structured sparsity"):
  with members:
    - "introduction to online convex optimization"
    - "efficient regret minimization in non convex games"
    - "graph sparse logistic regression"
    - "learning with feature evolvable streams"
    - "unregularized online learning algorithms with general loss functions"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"

Connected through hyperedge HE-6 (induced by source paper "online learning for min max discrete problems"):
  with members:
    - "introduction to online convex optimization"
    - "efficient online linear optimization with approximation algorithms"
    - "oracle efficient online learning and auction design"
    - "efficient algorithms for adversarial contextual learning"
    - "the computational power of optimization in online learning"
    - "learning in auctions regret is hard envy is easy"
    - "secretary problems with convex costs"

Connected through hyperedge HE-7 (induced by source paper "online non convex learning following the perturbed leader is optimal"):
  with members:
    - "introduction to online convex optimization"
    - "efficient regret minimization in non convex games"
    - "intriguing properties of neural networks"
    - "adaptive online prediction by following the perturbed leader"

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)