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 "on the whittle index for restless multi armed hidden markov bandits"):
  Abstract: We consider a restless multi-armed bandit in which each arm can be in one of two states. When an arm is sampled, the state of the arm is not available to the sampler. Instead, a binary signal with a known randomness that depends on the state of the arm is available. No signal is available if the arm is not sampled. An arm-dependent reward is accrued from each sampling. In each time step, each arm changes state according to known transition probabilities which in turn depend on whether the arm is sampled or not sampled. Since the state of the arm is never visible and has to be inferred from the current belief and a possible binary signal, we call this the hidden Markov bandit. Our interest is in a policy to select the arm(s) in each time step that maximizes the infinite horizon discounted reward. Specifically, we seek the use of Whittle's index in selecting the arms. We first analyze the single-armed bandit and show that in general, it admits an approximate threshold-type optimal policy when there is a positive reward for the `no-sample' action. We also identify several special cases for which the threshold policy is indeed the optimal policy. Next, we show that such a single-armed bandit also satisfies an approximate-indexability property. For the case when the single-armed bandit admits a threshold-type optimal policy, we perform the calculation of the Whittle index for each arm. Numerical examples illustrate the analytical results.

Includes members:
  Member "whittle index policy for crawling ephemeral content":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained restless bandits for dynamic scheduling in cyber physical systems")
  Member "congestion control of tcp flows in internet routers by means of index policy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning unknown service rates in queues a multi armed bandit approach")
  Member "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sufficient conditions on the optimality of myopic sensing in opportunistic channel access a unifying framework")
  Member "asymptotically optimal downlink scheduling over markovian fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constrained restless bandits for dynamic scheduling in cyber physical systems")
  Member "exploiting channel memory for joint estimation and scheduling in downlink networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low complexity optimal scheduling over time correlated fading channels with arq feedback")
  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")
  Member "joint design and separation principle for opportunistic spectrum access in the presence of sensing errors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cost aware learning and optimization for opportunistic spectrum access")

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.SY(Systems and Control)