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 "millimeter wave energy harvesting"):
  Abstract: The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions.

Includes members:
  Member "wireless powered communication opportunities and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "performance analysis of ambient rf energy harvesting with repulsive point process modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint energy and sinr coverage in spatially clustered rf powered iot network")
  Member "energy harvesting wireless communications a review of recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "powering the next billion devices with wi fi":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards flexible wireless charging for medical implants using distributed antenna system")
  Member "the human body and millimeter wave wireless communication systems interactions and implications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "propagation measurement system and approach at 140 ghz moving to 6g and above 100 ghz")
  Member "tractable model for rate in self backhauled millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "coverage and rate analysis for millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")

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)