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 "on maximizing sampling time of rf harvesting sensor nodes over random channel gains"):
  Abstract: In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a {\em sampling time} to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the {\em expected} penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: {\em single} and {\em multi} time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next $T$ slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.

Includes members:
  Member "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model":
    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 "powering the next billion devices with wi fi":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards flexible wireless charging for medical implants using distributed antenna system")
  Member "joint beamforming design and time allocation for wireless powered communication networks":
    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 "wireless networks with rf energy harvesting a contemporary survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "throughput maximization in wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on maximizing information reliability in wireless powered cooperative networks")
  Member "relaying protocols for wireless energy harvesting and information processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on maximizing information reliability in wireless powered cooperative networks")
  Member "multi antenna wireless powered communication with energy beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")

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.NI(Networking and Internet Architecture)