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: "iterate averaging as regularization for stochastic gradient descent"
  Abstract: We propose and analyze a variant of the classic Polyak-Ruppert averaging scheme, broadly used in stochastic gradient methods. Rather than a uniform average of the iterates, we consider a weighted average, with weights decaying in a geometric fashion. In the context of linear least squares regression, we show that this averaging scheme has a the same regularizing effect, and indeed is asymptotically equivalent, to ridge regression. In particular, we derive finite-sample bounds for the proposed approach that match the best known results for regularized stochastic gradient methods.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "learning to optimize under non stationarity"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "make the minority great again first order regret bound for contextual bandits"
    - "kernel based methods for bandit convex optimization"

Connected through hyperedge HE-2 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"):
  with members:
    - "tight analyses for non smooth stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-3 (induced by source paper "distribution dependent analysis of gibbs erm principle"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "piecewise strong convexity of neural networks"
    - "local optimality and generalization guarantees for the langevin algorithm via empirical metastability"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "fisher rao metric geometry and complexity of neural networks"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"

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)