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 "an efficient high probability algorithm for linear bandits"):
  Abstract: For the linear bandit problem, we extend the analysis of algorithm CombEXP from [R. Combes, M. S. Talebi Mazraeh Shahi, A. Proutiere, and M. Lelarge. Combinatorial bandits revisited. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2116--2124. Curran Associates, Inc., 2015. URL this http URL] to the high-probability case against adaptive adversaries, allowing actions to come from an arbitrary polytope. We prove a high-probability regret of \(O(T^{2/3})\) for time horizon \(T\). While this bound is weaker than the optimal \(O(\sqrt{T})\) bound achieved by GeometricHedge in [P. L. Bartlett, V. Dani, T. Hayes, S. Kakade, A. Rakhlin, and A. Tewari. High-probability regret bounds for bandit online linear optimization. In 21th Annual Conference on Learning Theory (COLT 2008), July 2008. this http URL], CombEXP is computationally efficient, requiring only an efficient linear optimization oracle over the convex hull of the actions.

Includes members:
  Member "kernel based methods for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "an optimal algorithm for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs")
  Member "explore no more improved high probability regret bounds for non stochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "combinatorial bandits revisited":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "tight bounds for approximate carath eodory and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "regret in online combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "how to beat the adaptive multi armed bandit":
    also connected through other hyperedges:
      (none)

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.DS(Data Structures and Algorithms)