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: "an optimal algorithm for bandit convex optimization"
  Abstract: We consider the problem of online convex optimization against an arbitrary adversary with bandit feedback, known as bandit convex optimization. We give the first $\tilde{O}(\sqrt{T})$-regret algorithm for this setting based on a novel application of the ellipsoid method to online learning. This bound is known to be tight up to logarithmic factors. Our analysis introduces new tools in discrete convex geometry.

Connected through hyperedge HE-1 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "the mechanics of n player differentiable games"
    - "on convergence and stability of gans"
    - "faster rates for convex concave games"
    - "adversarial bandits with knapsacks"
    - "began boundary equilibrium generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (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"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "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-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"
    - "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"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-4 (induced by source paper "online continuous submodular maximization from full information to bandit feedback"):
  with members:
    - "black box submodular maximization discrete and continuous settings"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "online submodular maximization under a matroid constraint with application to learning assignments"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "projection free online learning"

Connected through hyperedge HE-5 (induced by source paper "wireless optimisation via convex bandits unlicensed lte wifi coexistence"):
  with members:
    - "introduction to online convex optimization"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "fair coexistence of scheduled and random access wireless networks unlicensed lte wifi"
    - "multi armed bandits with application to 5g small cells"
    - "kernel based methods for bandit convex optimization"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "combinatorial bandits revisited"
    - "unlicensed lte wifi coexistence is lbt inherently fairer than csat"

Connected through hyperedge HE-6 (induced by source paper "online newton step algorithm with estimated gradient"):
  with members:
    - "online learning a comprehensive survey"
    - "stochastic optimization with bandit sampling"
    - "online learning to rank in stochastic click models"
    - "kernel based methods for bandit convex optimization"
    - "on graduated optimization for stochastic non convex problems"
    - "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-7 (induced by source paper "risk averse stochastic convex bandit"):
  with members:
    - "introduction to online convex optimization"
    - "kernel based methods for bandit convex optimization"
    - "risk aversion in multi armed bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "recursive exponential weighting for online non convex optimization"):
  with members:
    - "bandits and experts in metric spaces"

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)