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 "equipping experts bandits with long term memory"):
  Abstract: We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth, 2002, by reducing the problem to achieving typical switching regret. Specifically, for the classical expert problem with $K$ actions and $T$ rounds, using our framework we develop various algorithms with a regret bound of order $\mathcal{O}(\sqrt{T(S\ln T + n \ln K)})$ compared to any sequence of experts with $S-1$ switches among $n \leq \min\{S, K\}$ distinct experts. In addition, by plugging specific adaptive algorithms into our framework we also achieve the best of both stochastic and adversarial environments simultaneously. This resolves an open problem of Warmuth and Koolen, 2014. Furthermore, we extend our results to the sparse multi-armed bandit setting and show both negative and positive results for long-term memory guarantees. As a side result, our lower bound also implies that sparse losses do not help improve the worst-case regret for contextual bandits, a sharp contrast with the non-contextual case.

Includes members:
  Member "beating stochastic and adversarial semi bandits optimally and simultaneously":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "improved path length regret bounds for bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandits with adversarial scaling")
  Member "more adaptive algorithms for adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack")
  Member "tracking the best expert in non stationary stochastic environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "sparsity variance and curvature in multi armed bandits":
    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 "strongly adaptive online learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "the best of both worlds stochastic and adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bandits with adversarial scaling")

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)