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: "the best of both worlds stochastic and adversarial bandits"
  Abstract: We present a new bandit algorithm, SAO (Stochastic and Adversarial Optimal), whose regret is, essentially, optimal both for adversarial rewards and for stochastic rewards. Specifically, SAO combines the square-root worst-case regret of Exp3 (Auer et al., SIAM J. on Computing 2002) and the (poly)logarithmic regret of UCB1 (Auer et al., Machine Learning 2002) for stochastic rewards. Adversarial rewards and stochastic rewards are the two main settings in the literature on (non-Bayesian) multi-armed bandits. Prior work on multi-armed bandits treats them separately, and does not attempt to jointly optimize for both. Our result falls into a general theme of achieving good worst-case performance while also taking advantage of "nice" problem instances, an important issue in the design of algorithms with partially known inputs.

Connected through hyperedge HE-1 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "improved path length regret bounds for bandits"
    - "competing bandits the perils of exploration under competition"
    - "factored bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "bandit regret scaling with the effective loss range"

Connected through hyperedge HE-2 (induced by source paper "adversarial attacks on linear contextual bandits"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "data poisoning attacks on stochastic bandits"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "targeted attacks on deep reinforcement learning agents through adversarial observations"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "data poisoning attacks in contextual bandits"
    - "adversarial bandits with knapsacks"

Connected through hyperedge HE-3 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"):
  with members:
    - "data poisoning attacks on stochastic bandits"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "algorithms for multi armed bandit problems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "decentralized online learning algorithms for opportunistic spectrum access"

Connected through hyperedge HE-4 (induced by source paper "model selection in contextual stochastic bandit problems"):
  with members:
    - "model selection for contextual bandits"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "corralling a band of bandit algorithms"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"

Connected through hyperedge HE-6 (induced by source paper "corruption robust exploration in episodic reinforcement learning"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "recent advances in algorithmic high dimensional robust statistics"

Connected through hyperedge HE-7 (induced by source paper "better algorithms for stochastic bandits with adversarial corruptions"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "stochastic bandits robust to adversarial corruptions"
    - "efficient algorithms for outlier robust regression"
    - "tsallis inf an optimal algorithm for stochastic and adversarial bandits"
    - "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
    - "learning geometric concepts with nasty noise"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "learning from untrusted data"

Connected through hyperedge HE-8 (induced by source paper "thompson sampling for adversarial bit prediction"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "towards optimal algorithms for prediction with expert advice"
    - "an information theoretic analysis of thompson sampling"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"

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)