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: "sparsity variance and curvature in multi armed bandits"
  Abstract: In (online) learning theory the concepts of sparsity, variance and curvature are well-understood and are routinely used to obtain refined regret and generalization bounds. In this paper we further our understanding of these concepts in the more challenging limited feedback scenario. We consider the adversarial multi-armed bandit and linear bandit settings and solve several open problems pertaining to the existence of algorithms with favorable regret bounds under the following assumptions: (i) sparsity of the individual losses, (ii) small variation of the loss sequence, and (iii) curvature of the action set. Specifically we show that (i) for $s$-sparse losses one can obtain $\tilde{O}(\sqrt{s T})$-regret (solving an open problem by Kwon and Perchet), (ii) for loss sequences with variation bounded by $Q$ one can obtain $\tilde{O}(\sqrt{Q})$-regret (solving an open problem by Kale and Hazan), and (iii) for linear bandit on an $\ell_p^n$ ball one can obtain $\tilde{O}(\sqrt{n T})$-regret for $p \in [1,2]$ and one has $\tilde{\Omega}(n \sqrt{T})$-regret for $p>2$ (solving an open problem by Bubeck, Cesa-Bianchi and Kakade). A key new insight to obtain these results is to use regularizers satisfying more refined conditions than general self-concordance

Connected through hyperedge HE-1 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback"):
  with members:
    - "equipping experts bandits with long term memory"
    - "first order regret analysis of thompson sampling"
    - "bandits with feedback graphs and switching costs"
    - "improved path length regret bounds for bandits"
    - "online learning with feedback graphs and switching costs"
    - "make the minority great again first order regret bound for contextual bandits"
    - "online learning with graph structured feedback against adaptive adversaries"
    - "small loss bounds for online learning with partial information"

Connected through hyperedge HE-2 (induced by source paper "exploration by optimisation in partial monitoring"):
  with members:
    - "on first order bounds variance and gap dependent bounds for adversarial bandits"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "connections between mirror descent thompson sampling and the information ratio"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "improved path length regret bounds for bandits"
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "more adaptive algorithms for adversarial bandits"

Connected through hyperedge HE-3 (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"
    - "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"
    - "towards optimal algorithms for prediction with expert advice"

Connected through hyperedge HE-4 (induced by source paper "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "introduction to online convex optimization"
    - "bandit online learning with unknown delays"
    - "thompson sampling for combinatorial semi bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "collaborative filtering bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-5 (induced by source paper "bandit principal component analysis"):
  with members:
    - "bilinear bandits with low rank structure"
    - "introduction to online convex optimization"
    - "more adaptive algorithms for adversarial bandits"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "stochastic low rank bandits"
    - "corralling a band of bandit algorithms"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"

Connected through hyperedge HE-6 (induced by source paper "an information theoretic approach to minimax regret in partial monitoring"):
  with members:
    - "more adaptive algorithms for adversarial 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"
    - "towards minimax policies for online linear optimization with bandit feedback"

Connected through hyperedge HE-7 (induced by source paper "equipping experts bandits with long term memory"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "strongly adaptive online learning"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-8 (induced by source paper "beating stochastic and adversarial semi bandits optimally and simultaneously"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "combinatorial bandits revisited"
    - "first order regret bounds for combinatorial semi bandits"
    - "the best of both worlds stochastic and adversarial bandits"

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)