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: "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
  Abstract: In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes.

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"
    - "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter"
    - "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"
    - "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:
    - "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter"
    - "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:
    - "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter"
    - "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 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)