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 "communication over a time correlated channel with an energy harvesting transmitter"):
  Abstract: In this work, communication over a time-correlated point-to-point wireless channel is studied for an energy harvesting (EH) transmitter. In this model, we take into account the time and energy cost of acquiring channel state information. At the beginning of the time slot, the EH transmitter, has to choose among three possible actions: i) deferring the transmission to save its energy for future use, ii) transmitting without sensing, and iii) sensing the channel before transmission. At each time slot, the transmitter chooses one of the three possible actions to maximize the total expected discounted number of bits transmitted over an infinite time horizon. This problem can be formulated as a partially observable Markov decision process (POMDP) which is then converted to an ordinary MDP by introducing a belief on the channel state, and the optimal policy is shown to exhibit a threshold behavior on the belief state, with battery-dependent threshold values. Optimal threshold values and corresponding optimal performance are characterized through numerical simulations, and it is shown that having the sensing action and intelligently using it to track the channel state improves the achievable long-term throughput significantly.

Includes members:
  Member "energy harvesting wireless networks with correlated energy sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "channel sensing and communication over a time correlated channel with an energy harvesting transmitter")
  Member "channel sensing and communication over a time correlated channel with an energy harvesting transmitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless energy and information transfer in networks with hybrid arq")
  Member "distributed opportunistic scheduling for energy harvesting based wireless networks a two stage probing approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy harvesting wireless networks with correlated energy sources")
  Member "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach")
  Member "data driven stochastic models and policies for energy harvesting sensor communications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finite horizon throughput maximization and sensing optimization in wireless powered devices over fading channels")
  Member "a learning theoretic approach to energy harvesting communication system optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "optimal energy allocation for wireless communications with energy harvesting constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing 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.PF(Performance)