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: "bandit convex optimization sqrt t regret in one dimension"
  Abstract: We analyze the minimax regret of the adversarial bandit convex optimization problem. Focusing on the one-dimensional case, we prove that the minimax regret is $\widetilde\Theta(\sqrt{T})$ and partially resolve a decade-old open problem. Our analysis is non-constructive, as we do not present a concrete algorithm that attains this regret rate. Instead, we use minimax duality to reduce the problem to a Bayesian setting, where the convex loss functions are drawn from a worst-case distribution, and then we solve the Bayesian version of the problem with a variant of Thompson Sampling. Our analysis features a novel use of convexity, formalized as a "local-to-global" property of convex functions, that may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "bandit convex optimization in non stationary environments"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "corralling a band of bandit algorithms"
    - "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"

Connected through hyperedge HE-2 (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"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "towards optimal algorithms for prediction with expert advice"

Connected through hyperedge HE-3 (induced by source paper "improved regret bounds for projection free bandit convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (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"
    - "prediction by random walk perturbation"
    - "bandits with switching costs t 2 3 regret"
    - "online learning with switching costs and other adaptive adversaries"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-5 (induced by source paper "first order bayesian regret analysis of thompson sampling"):
  with members:
    - "feedback graph regret bounds for thompson sampling and ucb"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "make the minority great again first order regret bound for contextual bandits"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "small loss bounds for online learning with partial information"
    - "online learning with feedback graphs beyond bandits"
    - "an information theoretic analysis of thompson sampling"
    - "regret in online combinatorial optimization"

Connected through hyperedge HE-6 (induced by source paper "multidimensional dynamic pricing for welfare maximization"):
  with members:
    - "do prices coordinate markets"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "combinatorial auctions via posted prices"
    - "bandits with knapsacks"
    - "efficiently learning from revealed preference"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)