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: "backscatter assisted relaying in wireless powered communications network"
  Abstract: This paper studies a novel cooperation method in a two-user wireless powered communication network (WPCN), in which one hybrid access point (HAP) broadcasts wireless energy to two distributed wireless devices (WDs), while the WDs use the harvested energy to transmit their independent information to the HAP. To tackle the user unfairness problem caused by the near-far effect in WPCN, we allow the WD with the stronger WD-to-HAP channel to use part of its harvested energy to help relay the other weaker user's information to the HAP. In particular, we exploit the use of backscatter communication during the wireless energy transfer phase such that the helping relay user can harvest energy and receive the information from the weaker user simultaneously. We derive the maximum common throughput performance by jointly optimizing the time duration and power allocations on wireless energy and information transmissions. Our simulation results demonstrate that the backscatter-assisted cooperation scheme can effectively improve the throughput fairness performance in WPCNs.

Connected through hyperedge HE-1 (induced by source paper "reusing wireless power transfer for backscatter assisted relaying in wpcns"):
  with members:
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "joint tx power allocation and rx power splitting for swipt system with multiple nonlinear energy harvesting circuits"
    - "multi antenna enabled cluster based cooperation in wireless powered communication networks"
    - "distributed scheduling in wireless powered communication network protocol design and performance analysis"
    - "ambient backscatter communications a contemporary survey"
    - "user cooperation for enhanced throughput fairness in wireless powered communication networks"
    - "distributed charging control in broadband wireless power transfer networks"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"

Connected through hyperedge HE-2 (induced by source paper "throughput maximization for ambient backscatter communication a reinforcement learning approach"):
  with members:
    - "reusing wireless power transfer for backscatter assisted cooperation in wpcn"
    - "wireless powered communication networks an overview"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"

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)