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: "online learning stability and stochastic gradient descent"
  Abstract: In batch learning, stability together with existence and uniqueness of the solution corresponds to well-posedness of Empirical Risk Minimization (ERM) methods; recently, it was proved that CVloo stability is necessary and sufficient for generalization and consistency of ERM ([2]). In this note, we introduce CVon stability, which plays a similar role in online learning. We show that stochastic gradient descent (SDG) with the usual hypotheses is CVon stable and we then discuss the implications of CVon stability for convergence of SGD.

Connected through hyperedge HE-1 (induced by source paper "maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream"):
  with members:
    - "inferring parameters and structure of latent variable models by variational bayes"
    - "hierarchical exploration for accelerating contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "online learning via the differential privacy lens"):
  with members:
    - "introduction to online convex optimization"
    - "private pac learning implies finite littlestone dimension"
    - "make the minority great again first order regret bound for contextual bandits"
    - "achieving privacy in the adversarial multi armed bandit"
    - "the price of differential privacy for online learning"
    - "adaptive learning with robust generalization guarantees"
    - "fighting bandits with a new kind of smoothness"
    - "on the generalization properties of differential privacy"

Connected through hyperedge HE-3 (induced by source paper "online learning via differential privacy"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "pca with gaussian perturbations"
    - "fighting bandits with a new kind of smoothness"
    - "on the generalization properties of differential privacy"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "first order regret bounds for combinatorial semi bandits"

Connected through hyperedge HE-4 (induced by source paper "data dependent stability of stochastic gradient descent"):
  with members:
    - "fast rates by transferring from auxiliary hypotheses"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-5 (induced by source paper "to drop or not to drop robustness consistency and differential privacy properties of dropout"):
  with members:
    - "on the inductive bias of dropout"
    - "intriguing properties of neural networks"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "a pac bayesian tutorial with a dropout bound"
    - "calibrating data to sensitivity in private data analysis"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "lock in feedback in sequential experiments"):
  with members:
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-7 (induced by source paper "thompson sampling with the online bootstrap"):
  with members:
    - "hierarchical exploration for accelerating contextual bandits"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "the interplay between stability and regret in online learning"):
  with members:
    - "almost everywhere algorithmic stability and generalization error"
    - "differentially private online learning"
    - "stability conditions for online learnability"

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)