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 "downlink scheduling over markovian fading channels"):
  Abstract: We consider the scheduling problem in downlink wireless networks with heterogeneous, Markov-modulated, ON/OFF channels. It is well-known that the performance of scheduling over fading channels relies heavily on the accuracy of the available Channel State Information (CSI), which is costly to acquire. Thus, we consider the CSI acquisition via a practical ARQ-based feedback mechanism whereby channel states are revealed at the end of only scheduled users' transmissions. In the assumed presence of temporally-correlated channel evolutions, the desired scheduler must optimally balance the exploitation-exploration trade-off, whereby it schedules transmissions both to exploit those channels with up-to-date CSI and to explore the current state of those with outdated CSI. #R##N#In earlier works, Whittle's Index Policy had been suggested as a low-complexity and high-performance solution to this problem. However, analyzing its performance in the typical scenario of statistically heterogeneous channel state processes has remained elusive and challenging, mainly because of the highly-coupled and complex dynamics it possesses. In this work, we overcome these difficulties to rigorously establish the asymptotic optimality properties of Whittle's Index Policy in the limiting regime of many users. More specifically: (1) we prove the local optimality of Whittle's Index Policy, provided that the initial state of the system is within a certain neighborhood of a carefully selected state; (2) we then establish the global optimality of Whittle's Index Policy under a recurrence assumption that is verified numerically for our problem. These results establish that Whittle's Index Policy possesses analytically provable optimality characteristics for scheduling over heterogeneous and temporally-correlated channels.

Includes members:
  Member "asymptotically optimal downlink scheduling over markovian fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained restless bandits for dynamic scheduling in cyber physical systems")
  Member "joint scheduling and resource allocation in the ofdma downlink utility maximization under imperfect channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "scheduling with rate adaptation under incomplete knowledge of channel estimator statistics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low complexity optimal scheduling over time correlated fading channels with arq feedback")
  Member "exploiting channel memory for joint estimation and scheduling in downlink networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks")
  Member "approximation algorithms for restless bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regime switching bandits")
  Member "reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

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.NI(Networking and Internet Architecture)