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 "optimal and low complexity dynamic spectrum access for rf powered ambient backscatter system with online reinforcement learning"):
  Abstract: Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this article, we propose an optimal and low-complexity dynamic spectrum access framework for RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals (from incumbent users), but also backscatters these signals to its receiver for data transmission. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment, but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.

Includes members:
  Member "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint time scheduling and transaction fee selection in blockchain based rf powered backscatter cognitive radio network")
  Member "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint time scheduling and transaction fee selection in blockchain based rf powered backscatter cognitive radio network")
  Member "energy efficient scheduling for loss tolerant iot applications with uninformed transmitter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on optimizing power allocation for reliable communication over fading channels with uninformed transmitter")
  Member "inference from randomized transmissions by many backscatter sensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "over the air computation systems optimization analysis and scaling laws")
  Member "ambient backscatter communications a contemporary survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "irs based wireless jamming attacks when jammers can attack without power")
  Member "mimo wiretap channel under receiver side power constraints with applications to wireless power transfer and cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the secrecy capacity of a full duplex wirelessly powered communication system")
  Member "wirelessly powered backscatter communication networks modeling coverage and capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "clustered millimeter wave networks with non orthogonal multiple access")
  Member "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")

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)