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 "spectral and energy efficient wireless powered iot networks noma or tdma"):
  Abstract: Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future Internet-of-Things (IoT). Meanwhile, non-orthogonal multiple access (NOMA) has been proposed to improve the system spectral efficiency (SE) of the fifth-generation (5G) networks by allowing concurrent transmissions of multiple users in the same spectrum. As such, NOMA has been recently considered for the uplink of WPCNs based IoT networks with a massive number of devices. However, simultaneous transmissions in NOMA may also incur more transmit energy consumption as well as circuit energy consumption in practice which is critical for energy constrained IoT devices. As a result, compared to orthogonal multiple access schemes such as time-division multiple access (TDMA), whether the SE can be improved and/or the total energy consumption can be reduced with NOMA in such a scenario still remains unknown. To answer this question, we first derive the optimal time allocations for maximizing the SE of a TDMA-based WPCN (T-WPCN) and a NOMA-based WPCN (N-WPCN), respectively. Subsequently, we analyze the total energy consumption as well as the maximum SE achieved by these two networks. Surprisingly, it is found that N-WPCN not only consumes more energy, but also is less spectral efficient than T-WPCN. Simulation results verify our theoretical findings and unveil the fundamental performance bottleneck, i.e., "worst user bottleneck problem", in multiuser NOMA systems.

Includes members:
  Member "wireless powered cooperative jamming for secure ofdm system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "securing uav communications via joint trajectory and power control")
  Member "energy efficient low power circuits for wireless energy and data transfer in iot sensor nodes":
    also connected through other hyperedges:
      (none)
  Member "fundamental green tradeoffs progresses challenges and impacts on 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beamforming optimization for intelligent reflecting surface assisted mimo a sum path gain maximization approach")
  Member "wireless powered communications with non orthogonal multiple access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interplay between noma and other emerging technologies a survey")
  Member "an overview of sustainable green 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross layer scheduling and beamforming in smart grid powered cellular networks with heterogeneous energy coordination")
  Member "wireless powered communication networks an overview":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "energy efficient resource allocation for wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "common throughput maximization for uav enabled interference channel with wireless powered communications")
  Member "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance analysis of intelligent reflecting surface assisted noma networks")

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)