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 "joint time scheduling and transaction fee selection in blockchain based rf powered backscatter cognitive radio network"):
  Abstract: In this paper, we develop a new framework called blockchain-based Radio Frequency (RF)-powered backscatter cognitive radio network. In the framework, IoT devices as secondary transmitters transmit their sensing data to a secondary gateway by using the RF-powered backscatter cognitive radio technology. The data collected at the gateway is then sent to a blockchain network for further verification, storage and processing. As such, the framework enables the IoT system to simultaneously optimize the spectrum usage and maximize the energy efficiency. Moreover, the framework ensures that the data collected from the IoT devices is verified, stored and processed in a decentralized but in a trusted manner. To achieve the goal, we formulate a stochastic optimization problem for the gateway under the dynamics of the primary channel, the uncertainty of the IoT devices, and the unpredictability of the blockchain environment. In the problem, the gateway jointly decides (i) the time scheduling, i.e., the energy harvesting time, backscatter time, and transmission time, among the IoT devices, (ii) the blockchain network, and (iii) the transaction fee rate to maximize the network throughput while minimizing the cost. To solve the stochastic optimization problem, we then propose to employ, evaluate, and assess the Deep Reinforcement Learning (DRL) with Dueling Double Deep Q-Networks (D3QN) to derive the optimal policy for the gateway. The simulation results clearly show that the proposed solution outperforms the conventional baseline approaches such as the conventional Q-Learning algorithm and non-learning algorithms in terms of network throughput and convergence speed. Furthermore, the proposed solution guarantees that the data is stored in the blockchain network at a reasonable cost.

Includes members:
  Member "deep reinforcement learning for time scheduling in rf powered backscatter cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resource allocation in mobility aware federated learning networks a deep reinforcement learning approach")
  Member "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal and low complexity dynamic spectrum access for rf powered ambient backscatter system with online reinforcement learning")
  Member "riding on the primary a new spectrum sharing paradigm for wireless powered iot devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "throughput maximization for uav aided backscatter communication networks")
  Member "wireless powered asynchronous backscatter networks with sporadic short packets performance analysis and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal resource allocation in full duplex ambient backscatter communication networks for wireless powered iot")
  Member "deep reinforcement learning for dynamic multichannel access in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal and low complexity dynamic spectrum access for rf powered ambient backscatter system with online reinforcement learning")
  Member "blockchained on device federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "joint transaction transmission and channel selection in cognitive radio based blockchain networks a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blockchain for 5g and beyond networks a state of the art survey")

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)