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: "efficient regret minimization in non convex games"
  Abstract: We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. We give gradient-based methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework.

Connected through hyperedge HE-1 (induced by source paper "rnn based online learning an efficient first order optimization algorithm with a convergence guarantee"):
  with members:
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "on the convergence rate of training recurrent neural networks"
    - "a local regret in nonconvex online learning"
    - "learning with random learning rates"
    - "stable recurrent models"
    - "lstm a search space odyssey"
    - "training deep networks without learning rates through coin betting"
    - "gradient descent learns linear dynamical systems"

Connected through hyperedge HE-2 (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"
    - "learning in non convex games with an optimization oracle"
    - "on convergence and stability of gans"
    - "an online learning approach to generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "no regret non convex online meta learning"):
  with members:
    - "distribution agnostic model agnostic meta learning"
    - "online meta learning"
    - "on first order meta learning algorithms"
    - "how to train your maml"
    - "matching networks for one shot learning"
    - "you only look once unified real time object detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "stabilizing generative adversarial network training a survey"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "multi objective training of generative adversarial networks with multiple discriminators"
    - "evaluating gans via duality"
    - "convergence problems with generative adversarial networks gans"
    - "the relativistic discriminator a key element missing from standard gan"
    - "evolutionary generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"

Connected through hyperedge HE-5 (induced by source paper "dual averaging method for online graph structured sparsity"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "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 "dynamic local regret for non convex online forecasting"):
  with members:
    - "spectral filtering for general linear dynamical systems"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "online forecasting matrix factorization"
    - "dynamic regret of strongly adaptive methods"
    - "strongly adaptive online learning"
    - "online learning for time series prediction"
    - "a spectral algorithm for learning hidden markov models"

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"
    - "learning in non convex games with an optimization oracle"
    - "intriguing properties of neural networks"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-8 (induced by source paper "online adaptive principal component analysis and its extensions"):
  with members:
    - "spectral filtering for general linear dynamical systems"
    - "global convergence of policy gradient methods for linearized control problems"
    - "online convex optimization for cumulative constraints"
    - "mirror descent meets fixed share and feels no regret"

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)