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: "training deep networks without learning rates through coin betting"
  Abstract: Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning-rate-free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms.

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"
    - "efficient regret minimization in non convex games"
    - "gradient descent learns linear dynamical systems"

Connected through hyperedge HE-2 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"

Connected through hyperedge HE-3 (induced by source paper "a modern introduction to online learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "black box reductions for parameter free online learning in banach spaces"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "fighting bandits with a new kind of smoothness"
    - "second order quantile methods for experts and combinatorial games"
    - "prediction with advice of unknown number of experts"

Connected through hyperedge HE-4 (induced by source paper "recurrent neural networks for time series forecasting current status and future directions"):
  with members:
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "sales demand forecast in e commerce using a long short term memory neural network methodology"
    - "gluonts probabilistic time series models in python"
    - "temporal pattern attention for multivariate time series forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "matrix free preconditioning in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "the case for full matrix adaptive regularization"
    - "attention is all you need"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"
    - "coin betting and parameter free online learning"

Connected through hyperedge HE-6 (induced by source paper "adaptive scale invariant online algorithms for learning linear models"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "scale free algorithms for online linear optimization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

Connected through hyperedge HE-7 (induced by source paper "parameter free online convex optimization with sub exponential noise"):
  with members:
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "adaptive online learning"
    - "second order quantile methods for experts and combinatorial games"

Connected through hyperedge HE-8 (induced by source paper "adaptive regret of convex and smooth functions"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "scale free online learning"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"

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)