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 "learning in restless multi armed bandits via adaptive arm sequencing rules"):
  Abstract: We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Markovian dynamics. The reward state of passive arms (which are not chosen to play at time t) evolves according to an arbitrary unknown random process. The objective is an arm-selection policy that minimizes the regret, defined as the reward loss with respect to a player that always plays the most rewarding arm. This class of RMAB problems has been studied recently in the context of communication networks and financial investment applications. We develop a strategy that selects arms to be played in a consecutive manner, dubbed Adaptive Sequencing Rules (ASR) algorithm. The sequencing rules for selecting arms under the ASR algorithm are adaptively updated and controlled by the current sample reward means. By designing judiciously the adaptive sequencing rules, we show that the ASR algorithm achieves a logarithmic regret order with time, and a finite-sample bound on the regret is established. Although existing methods have shown a logarithmic regret order with time in this RMAB setting, the theoretical analysis shows a significant improvement in the regret scaling with respect to the system parameters under ASR. Extensive simulation results support the theoretical study and demonstrate strong performance of the algorithm as compared to existing methods.

Includes members:
  Member "deep multi user reinforcement learning for distributed dynamic spectrum access":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pops policy pruning and shrinking for deep reinforcement learning")
  Member "satisficing in multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lexicographic multiarmed bandit")
  Member "distributed game theoretic optimization and management of multichannel aloha networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks")
  Member "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimax optimal algorithms for adversarial bandit problem with multiple plays")
  Member "multi channel opportunistic access a case of restless bandits with multiple plays":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cost aware learning and optimization for opportunistic spectrum access")
  Member "on myopic sensing for multi channel opportunistic access structure optimality and performance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks")

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)