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 "more adaptive algorithms for adversarial bandits"):
  Abstract: We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem). When instantiated differently, our algorithm achieves various new data-dependent regret bounds improving previous works. Examples include: 1) a regret bound depending on the variance of only the best arm; 2) a regret bound depending on the first-order path-length of only the best arm; 3) a regret bound depending on the sum of first-order path-lengths of all arms as well as an important negative term, which together lead to faster convergence rates for some normal form games with partial feedback; 4) a regret bound that simultaneously implies small regret when the best arm has small loss and logarithmic regret when there exists an arm whose expected loss is always smaller than those of others by a fixed gap (e.g. the classic i.i.d. setting). In some cases, such as the last two results, our algorithm is completely parameter-free. #R##N#The main idea of our algorithm is to apply the optimism and adaptivity techniques to the well-known Online Mirror Descent framework with a special log-barrier regularizer. The challenges are to come up with appropriate optimistic predictions and correction terms in this framework. Some of our results also crucially rely on using a sophisticated increasing learning rate schedule.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  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 "small loss bounds for online learning with partial information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bandits with adversarial scaling")
  Member "sparsity variance and curvature in multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback")
  Member "corralling a band of bandit algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback")
  Member "metagrad multiple learning rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lipschitz and comparator norm adaptivity in online learning")
  Member "kernel based methods for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "coin betting and parameter free online learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive online learning with varying norms")

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)