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: "simultaneous model selection and optimization through parameter free stochastic learning"
  Abstract: Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been proposed to speed up their convergence, the model selection phase is often ignored. In fact, in theoretical works most of the time assumptions are made, for example, on the prior knowledge of the norm of the optimal solution, while in the practical world validation methods remain the only viable approach. In this paper, we propose a new kernel-based stochastic gradient descent algorithm that performs model selection while training, with no parameters to tune, nor any form of cross-validation. The algorithm builds on recent advancement in online learning theory for unconstrained settings, to estimate over time the right regularization in a data-dependent way. Optimal rates of convergence are proved under standard smoothness assumptions on the target function, using the range space of the fractional integral operator associated with the kernel.

Connected through hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms"):
  with members:
    - "extreme tensoring for low memory preconditioning"
    - "adaptive scale invariant online algorithms for learning linear models"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"

Connected through hyperedge HE-2 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "on the equivalence between quadrature rules and random features"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"

Connected through hyperedge HE-3 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "estimating learnability in the sublinear data regime"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"

Connected through hyperedge HE-4 (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"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"

Connected through hyperedge HE-5 (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"
    - "training deep networks without learning rates through coin betting"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "stochastic proximal auc maximization"):
  with members:
    - "introduction to online convex optimization"
    - "stochastic variance reduction methods for saddle point problems"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "one pass auc optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "stochastic optimization for machine learning"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-7 (induced by source paper "kernel truncated randomized ridge regression optimal rates and low noise acceleration"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "understanding deep learning requires rethinking generalization"
    - "loss minimization and parameter estimation with heavy tails"
    - "an identity for kernel ridge regression"

Connected through hyperedge HE-8 (induced by source paper "black box reductions for parameter free online learning in banach spaces"):
  with members:
    - "online learning sufficient statistics and the burkholder method"
    - "multi scale online learning and its applications to online auctions"
    - "metagrad multiple learning rates in online learning"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "a survey of algorithms and analysis for adaptive online learning"
    - "no regret algorithms for unconstrained online convex optimization"
    - "on the universality of online mirror descent"
    - "less regret via online conditioning"

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)