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: "a stochastic view of optimal regret through minimax duality"
  Abstract: We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. We show that the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. We use this expression to obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary.

Connected through hyperedge HE-1 (induced by source paper "connections between mirror descent thompson sampling and the information ratio"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs without the graphs"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"

Connected through hyperedge HE-2 (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"
    - "online linear optimization via smoothing"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "minimax regret of switching constrained online convex optimization no phase transition"):
  with members:
    - "online learning over a finite action set with limited switching"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "an optimal algorithm for bandit convex optimization"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "prediction by random walk perturbation"
    - "bandits with switching costs t 2 3 regret"
    - "online learning with switching costs and other adaptive adversaries"

Connected through hyperedge HE-4 (induced by source paper "distributed online optimization with long term constraints"):
  with members:
    - "introduction to online convex optimization"
    - "distributed online linear regression"
    - "online convex optimization for cumulative constraints"
    - "kernel based methods for bandit convex optimization"
    - "trading regret for efficiency online convex optimization with long term constraints"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-5 (induced by source paper "adaptivity and optimality a universal algorithm for online convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "adaptive online learning in dynamic environments"
    - "metagrad multiple learning rates in online learning"
    - "strongly adaptive online learning"

Connected through hyperedge HE-6 (induced by source paper "online learning with an almost perfect expert"):
  with members:
    - "online learning sufficient statistics and the burkholder method"
    - "towards optimal algorithms for prediction with expert advice"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "randomized rounding without solving the linear program"

Connected through hyperedge HE-7 (induced by source paper "online convex optimization for cumulative constraints"):
  with members:
    - "global convergence of policy gradient methods for linearized control problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "projection free online learning"
    - "trading regret for efficiency online convex optimization with long term constraints"

Connected through hyperedge HE-8 (induced by source paper "dynamic regret of strongly adaptive methods"):
  with members:
    - "improved dynamic regret for non degeneracy functions"
    - "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"
    - "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)