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: "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter"
  Abstract: For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior- and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods.

Connected through hyperedge HE-1 (induced by source paper "joint time scheduling and transaction fee selection in blockchain based rf powered backscatter cognitive radio network"):
  with members:
    - "deep reinforcement learning for time scheduling in rf powered backscatter cognitive radio networks"
    - "riding on the primary a new spectrum sharing paradigm for wireless powered iot devices"
    - "wireless powered asynchronous backscatter networks with sporadic short packets performance analysis and optimization"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
    - "blockchained on device federated learning"
    - "joint transaction transmission and channel selection in cognitive radio based blockchain networks a deep reinforcement learning approach"
    - "a blockchain based approach for data accountability and provenance tracking"

Connected through hyperedge HE-2 (induced by source paper "optimal and low complexity dynamic spectrum access for rf powered ambient backscatter system with online reinforcement learning"):
  with members:
    - "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
    - "energy efficient scheduling for loss tolerant iot applications with uninformed transmitter"
    - "inference from randomized transmissions by many backscatter sensors"
    - "ambient backscatter communications a contemporary survey"
    - "mimo wiretap channel under receiver side power constraints with applications to wireless power transfer and cognitive radio"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"
    - "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis"
    - "opportunistic wireless energy harvesting in cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for time scheduling in rf powered backscatter cognitive radio networks"):
  with members:
    - "riding on the primary a new spectrum sharing paradigm for wireless powered iot devices"
    - "wireless powered asynchronous backscatter networks with sporadic short packets performance analysis and optimization"
    - "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
    - "deep reinforcement learning with double q learning"

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.NI(Networking and Internet Architecture)