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 "regime switching bandits"):
  Abstract: We study a multi-armed bandit problem where the rewards exhibit regime-switching. Specifically, the distributions of the random rewards generated from all arms depend on a common underlying state modeled as a finite-state Markov chain. The agent does not observe the underlying state and has to learn the unknown transition probability matrix as well as the reward distribution. We propose an efficient learning algorithm for this problem, building on spectral method-of-moments estimations for hidden Markov models and upper confidence bound methods for reinforcement learning. We also establish $O(T^{2/3}\sqrt{\log T})$ bound on the regret of the proposed learning algorithm where $T$ is the unknown horizon. Finally, we conduct numerical experiments to illustrate the effectiveness of the learning algorithm.

Includes members:
  Member "a survey on practical applications of multi armed and contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a story of two streams reinforcement learning models from human behavior and neuropsychiatry")
  Member "restless hidden markov bandits with linear rewards":
    also connected through other hyperedges:
      (none)
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "learning to optimize under non stationarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "multi armed bandits for correlated markovian environments with smoothed reward feedback":
    also connected through other hyperedges:
      (none)
  Member "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "restless hidden markov bandits with linear rewards")
  Member "reinforcement learning of pomdp s using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "reinforcement learning of pomdps using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")

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)