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 crowd sensing joint power transfer sensing compression and transmission"):
  Abstract: Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer (WPT) for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performs data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.

Includes members:
  Member "privacy management and optimal pricing in people centric sensing":
    also connected through other hyperedges:
      (none)
  Member "wirelessly powered urban crowd sensing over wearables trading energy for data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "competitive data trading in wireless powered internet of things iot crowdsensing systems with blockchain")
  Member "wirelessly powered backscatter communication networks modeling coverage and capacity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clustered millimeter wave networks with non orthogonal multiple access")
  Member "communications and signals design for wireless power transmission":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "ambient rf energy harvesting in ultra dense small cell networks performance and trade offs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "full duplex mimo ofdm communication with self energy recycling")
  Member "resource allocation in wireless powered relay networks a bargaining game approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "simultaneous wireless information and power transfer under different csi acquisition schemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transmission delay minimization in wireless powered communication systems")
  Member "energy efficient mobile cloud computing powered by wireless energy transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mobile communications computing and caching resources optimization for coded caching with device computing")

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)