human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "tsallis inf an optimal algorithm for stochastic and adversarial bandits"):
  Abstract: We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and time horizon. 1 The algorithm is based on online mirror descent with Tsallis entropy regularizer. We provide a complete characterization of such algorithms and show that Tsallis entropy with power $\alpha=1/2$ achieves the goal. In addition, the proposed algorithm enjoys improved regret guarantees in two intermediate regimes: stochastic bandits with adversarial corruptions introduced by Lykouris et al., and the stochastically constrained adversary studied by Wei and Luo. The algorithm also achieves adversarial and stochastic optimality in the utility-based dueling bandit setting. We provide empirical evaluation of the algorithm demonstrating that it outperforms UCB1 and EXP3 in stochastic environments. We also provide examples of adversarial environments, where UCB1 and Thompson Sampling exhibit almost linear regret, whereas our algorithm suffers only "logarithmic" regret.

Includes members:
  Member "connections between mirror descent thompson sampling and the information ratio":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration by optimisation in partial monitoring")
  Member "better algorithms for stochastic bandits with adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks on linear contextual bandits")
  Member "stochastic bandits robust to adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "more adaptive algorithms for adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bandits with adversarial scaling")
  Member "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack")
  Member "corralling a band of bandit algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback")
  Member "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bandits with adversarial scaling")
  Member "fighting bandits with a new kind of smoothness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private stochastic convex optimization efficient algorithms for non smooth objectives")

Question: Please tell me which class the source paper 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)