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 "wirelessly powered backscatter communication networks modeling coverage and capacity"):
  Abstract: Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering this massive number of embedded devices is a key challenge of designing IoT since batteries increase the devices' form factors and battery recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In the network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad-hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle and reflection coefficient, as well as the PB density. The effects of the parameters on network performance are characterized.

Includes members:
  Member "modeling and performance analysis of clustered device to device networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing joint probabilistic caching and channel access for clustered d2d networks")
  Member "wireless powered communication opportunities and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "spatial throughput maximization of wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transmission delay minimization in wireless powered communication systems")
  Member "multi antenna wireless energy transfer for backscatter communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient uav backscatter communication with joint trajectory design and resource optimization")
  Member "cutting last wires for mobile communication by microwave power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control")
  Member "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "simultaneous information and energy transfer in large scale networks with without relaying":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relay selection scheduling and power control in wireless powered cooperative communication networks")
  Member "fundamentals of heterogeneous cellular networks with energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint energy and sinr coverage in spatially clustered rf powered iot network")

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.IT(Information Theory)