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 learning in large scale combinatorial semi bandits"):
  Abstract: A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we consider efficient learning in large-scale combinatorial semi-bandits with linear generalization, and as a solution, propose two learning algorithms called Combinatorial Linear Thompson Sampling (CombLinTS) and Combinatorial Linear UCB (CombLinUCB). Both algorithms are computationally efficient as long as the offline version of the combinatorial problem can be solved efficiently. We establish that CombLinTS and CombLinUCB are also provably statistically efficient under reasonable assumptions, by developing regret bounds that are independent of the problem scale (number of items) and sublinear in time. We also evaluate CombLinTS on a variety of problems with thousands of items. Our experiment results demonstrate that CombLinTS is scalable, robust to the choice of algorithm parameters, and significantly outperforms the best of our baselines.

Includes members:
  Member "cascading bandits learning to rank in the cascade model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "tight regret bounds for stochastic combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "learning to act greedily polymatroid semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascading bandits learning to rank in the cascade model")
  Member "matroid bandits fast combinatorial optimization with learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "an information theoretic analysis of thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "learning to optimize via posterior sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "efficient exploration and value function generalization in deterministic systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "an efficient algorithm for learning with semi bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")

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)