human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a hidden markov restless multi armed bandit model for playout recommendation systems"
  Abstract: We consider a restless multi-armed bandit (RMAB) in which there are two types of arms, say A and B. Each arm can be in one of two states, say $0$ or $1.$ Playing a type A arm brings it to state $0$ with probability one and not playing it induces state transitions with arm-dependent probabilities. Whereas playing a type B arm leads it to state $1$ with probability $1$ and not playing it gets state that dependent on transition probabilities of arm. Further, play of an arm generates a unit reward with a probability that depends on the state of the arm. The belief about the state of the arm can be calculated using a Bayesian update after every play. This RMAB has been designed for use in recommendation systems where the user's preferences depend on the history of recommendations. This RMAB can also be used in applications like creating of playlists or placement of advertisements. In this paper we formulate the long term reward maximization problem as infinite horizon discounted reward and average reward problem. We analyse the RMAB by first studying discounted reward scenario. We show that it is Whittle-indexable and then obtain a closed form expression for the Whittle index for each arm calculated from the belief about its state and the parameters that describe the arm. We next analyse the average reward problem using vanishing discounted approach and derive the closed form expression for Whittle index. For a RMAB to be useful in practice, we need to be able to learn the parameters of the arms. We present an algorithm derived from Thompson sampling scheme, that learns the parameters of the arms and also illustrate its performance numerically.

Connected through hyperedge HE-1 (induced by source paper "constrained restless bandits for dynamic scheduling in cyber physical systems"):
  with members:
    - "sequential decision making under uncertainty with dynamic resource constraints"
    - "sequential decision making with limited observation capability application to wireless networks"
    - "whittle index policy for crawling ephemeral content"
    - "asymptotically optimal downlink scheduling over markovian fading channels"

Connected through hyperedge HE-2 (induced by source paper "restless hidden markov bandits with linear rewards"):
  with members:
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "regret bounds for reinforcement learning via markov chain concentration"
    - "regret bounds for restless markov bandits"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-3 (induced by source paper "sequential decision making under uncertainty with dynamic resource constraints"):
  with members:
    - "sequential decision making with limited observation capability application to wireless networks"
    - "whittle index policy for crawling ephemeral content"
    - "asymptotically optimal downlink scheduling over markovian fading channels"

Connected through hyperedge HE-4 (induced by source paper "sequential decision making with limited observation capability application to wireless networks"):
  with members:
    - "opportunistic scheduling as restless bandits"
    - "multiuser scheduling in a markov modeled downlink using randomly delayed arq feedback"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "downlink scheduling over markovian fading channels"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Question: Please tell me which class the center node 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)