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 "beating stochastic and adversarial semi bandits optimally and simultaneously"):
  Abstract: We develop the first general semi-bandit algorithm that simultaneously achieves $\mathcal{O}(\log T)$ regret for stochastic environments and $\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge of the regime or the number of rounds $T$. The leading problem-dependent constants of our bounds are not only optimal in some worst-case sense studied previously, but also optimal for two concrete instances of semi-bandit problems. Our algorithm and analysis extend the recent work of (Zimmert & Seldin, 2019) for the special case of multi-armed bandit, but importantly requires a novel hybrid regularizer designed specifically for semi-bandit. Experimental results on synthetic data show that our algorithm indeed performs well uniformly over different environments. We finally provide a preliminary extension of our results to the full bandit feedback.

Includes members:
  Member "more adaptive algorithms for adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack")
  Member "sparsity variance and curvature in multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback")
  Member "combining adversarial guarantees and stochastic fast rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast rates for online prediction with abstention")
  Member "combinatorial bandits revisited":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "first order regret bounds for combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "the best of both worlds stochastic and adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)