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: "joint transaction transmission and channel selection in cognitive radio based blockchain networks a deep reinforcement learning approach"
  Abstract: To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.

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"
    - "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
    - "blockchained on device federated learning"
    - "a blockchain based approach for data accountability and provenance tracking"

Connected through hyperedge HE-2 (induced by source paper "blockchain for 5g and beyond networks a state of the art survey"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "a key 6g challenge and opportunity connecting the remaining 4 billions a survey on rural connectivity"
    - "on safeguarding privacy and security in the framework of federated learning"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "trustsas a trustworthy spectrum access system for the 3 5 ghz cbrs band"
    - "blockchain enabled authentication handover with efficient privacy protection in sdn based 5g networks"
    - "6g wireless communication systems applications requirements technologies challenges and research directions"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"

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)