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 "adaptation to easy data in prediction with limited advice"):
  Abstract: We derive an online learning algorithm with improved regret guarantees for `easy' loss sequences. We consider two types of `easiness': (a) stochastic loss sequences and (b) adversarial loss sequences with small effective range of the losses. While a number of algorithms have been proposed for exploiting small effective range in the full information setting, Gerchinovitz and Lattimore [2016] have shown the impossibility of regret scaling with the effective range of the losses in the bandit setting. We show that just one additional observation per round is sufficient to circumvent the impossibility result. The proposed Second Order Difference Adjustments (SODA) algorithm requires no prior knowledge of the effective range of the losses, $\varepsilon$, and achieves an $O(\varepsilon \sqrt{KT \ln K}) + \tilde{O}(\varepsilon K \sqrt[4]{T})$ expected regret guarantee, where $T$ is the time horizon and $K$ is the number of actions. The scaling with the effective loss range is achieved under significantly weaker assumptions than those made by Cesa-Bianchi and Shamir [2018] in an earlier attempt to circumvent the impossibility result. We also provide a regret lower bound of $\Omega(\varepsilon\sqrt{T K})$, which almost matches the upper bound. In addition, we show that in the stochastic setting SODA achieves an $O\left(\sum_{a:\Delta_a>0} \frac{K\varepsilon^2}{\Delta_a}\right)$ pseudo-regret bound that holds simultaneously with the adversarial regret guarantee. In other words, SODA is safe against an unrestricted oblivious adversary and provides improved regret guarantees for at least two different types of `easiness' simultaneously.

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 "bandit regret scaling with the effective loss range":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack")
  Member "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bandits with adversarial scaling")
  Member "second order quantile methods for experts and combinatorial games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a modern introduction to online learning")
  Member "nonstochastic multi armed bandits with graph structured feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "multiarmed bandits with limited expert advice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to use learners advice")
  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)