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 "efficient and robust algorithms for adversarial linear contextual bandits"):
  Abstract: We consider an adversarial variant of the classic $K$-armed linear contextual bandit problem where the sequence of loss functions associated with each arm are allowed to change without restriction over time. Under the assumption that the $d$-dimensional contexts are generated i.i.d.~at random from a known distributions, we develop computationally efficient algorithms based on the classic Exp3 algorithm. Our first algorithm, RealLinExp3, is shown to achieve a regret guarantee of $\widetilde{O}(\sqrt{KdT})$ over $T$ rounds, which matches the best available bound for this problem. Our second algorithm, RobustLinExp3, is shown to be robust to misspecification, in that it achieves a regret bound of $\widetilde{O}((Kd)^{1/3}T^{2/3}) + \varepsilon \sqrt{d} T$ if the true reward function is linear up to an additive nonlinear error uniformly bounded in absolute value by $\varepsilon$. To our knowledge, our performance guarantees constitute the very first results on this problem setting.

Includes members:
  Member "weighted linear bandits for non stationary environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "comments on the du kakade wang yang lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "is a good representation sufficient for sample efficient reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "model selection for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "learning to optimize under non stationarity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "iterate averaging as regularization for stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares")
  Member "contextual bandits with surrogate losses margin bounds and efficient algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "make the minority great again first order regret bound for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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)