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: "wireless powered asynchronous backscatter networks with sporadic short packets performance analysis and optimization"
  Abstract: In the fifth generation era, the pervasive applications of Internet of Things and massive machine-type communications have initiated increasing research interests on the backscatter wireless powered communication (B-WPC) technique due to its ultra high energy efficiency and low cost. The ubiquitous B-WPC network is characterized by nodes with dynamic spatial positions and sporadic short packets, of which the performance has not been fully investigated. In this paper, we give a comprehensive analysis of a multi-antenna B-WPC network with sporadic short packets under a stochastic geometry framework. By exploiting a time-space Poisson point process model, the behavior of the network is well captured in a decentralized and asynchronous transmission way. We then analyze the energy and information outage performance in the energy harvest and backscatter modulation phases of the backscatter network, respectively. The optimal transmission slot length and division are obtained by maximizing the network-wide spatial throughput. Moreover, we find an interesting result that there exists the optimal tradeoff between the durations of the energy harvest and backscatter modulation phases for spatial throughput maximization. Numerical results are demonstrated to verify our analytical findings and show that this tradeoff region gets shrunk when the outage constraints become more stringent.

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"
    - "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 resource allocation in full duplex ambient backscatter communication networks for wireless powered iot"):
  with members:
    - "cooperative ambient backscatter communications for green internet of things"
    - "riding on the primary a new spectrum sharing paradigm for wireless powered iot devices"
    - "lora backscatter enabling the vision of ubiquitous connectivity"
    - "backscatter multiplicative multiple access systems fundamental limits and practical design"
    - "fm backscatter enabling connected cities and smart fabrics"
    - "modulation in the air backscatter communication over ambient ofdm carrier"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "multi antenna wireless energy transfer for backscatter communication systems"

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"
    - "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.IT(Information Theory)