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 linear optimization via smoothing"
  Abstract: We present a new optimization-theoretic approach to analyzing Follow-the-Leader style algorithms, particularly in the setting where perturbations are used as a tool for regularization. We show that adding a strongly convex penalty function to the decision rule and adding stochastic perturbations to data correspond to deterministic and stochastic smoothing operations, respectively. We establish an equivalence between "Follow the Regularized Leader" and "Follow the Perturbed Leader" up to the smoothness properties. This intuition leads to a new generic analysis framework that recovers and improves the previous known regret bounds of the class of algorithms commonly known as Follow the Perturbed Leader.

Connected through hyperedge HE-1 (induced by source paper "learning with differentiable perturbed optimizers"):
  with members:
    - "differentiable convex optimization layers"
    - "differentiation of blackbox combinatorial solvers"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "on measure concentration of random maximum a posteriori perturbations"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "on the partition function and random maximum a posteriori perturbations"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-2 (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"
    - "training deep networks without learning rates through coin betting"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "an information theoretic approach to minimax regret in partial monitoring"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "towards optimal algorithms for prediction with expert advice"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "further optimal regret bounds for thompson sampling"
    - "towards minimax policies for online linear optimization with bandit feedback"

Connected through hyperedge HE-4 (induced by source paper "introduction to online convex optimization"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "block coordinate frank wolfe optimization for structural svms"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "large scale convex minimization with a low rank constraint"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "tsallis inf an optimal algorithm for stochastic and adversarial bandits"):
  with members:
    - "connections between mirror descent thompson sampling and the information ratio"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "corralling a band of bandit algorithms"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "fighting bandits with a new kind of smoothness"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "follow the compressed leader faster online learning of eigenvectors and faster mmwu"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "sdca without duality regularization and individual convexity"
    - "even faster svd decomposition yet without agonizing pain"
    - "pca with gaussian perturbations"
    - "convergence of stochastic gradient descent for pca"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "spectral smoothing via random matrix perturbations"
    - "the noisy power method a meta algorithm with applications"

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)