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 "unlocking energy neutrality in energy harvesting wireless sensor networks an approach based on distributed compressed sensing"):
  Abstract: This paper advocates the use of the emerging distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) wireless sensor networks (WSN) with practical network lifetime and data gathering rates that are substantially higher than the state-of-the-art. The basis of our work is a centralized EH WSN architecture where the sensors convey data to a fusion center, using stylized models that capture the fact that the signals collected by different nodes can exhibit correlation and that the energy harvested by different nodes can also exhibit some degree of correlation. Via the probability of incorrect data reconstruction, we characterize the performance of both a compressive sensing (CS) and a DCS based approach to data acquisition and reconstruction. Moreover, we perform an in-depth comparison of the proposed DCS based approach against a state-of-the-art distributed source coding (DSC) system in terms of decoded data distortion versus harvested energy. These performance characterizations and comparisons embody the effect of various system phenomena and parameters such as signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, for an EH WSN consisting of eight SNs with our simple signal correlation and EH models, a target probability of incorrect reconstruction of $10^{-2}$, and under the same EH capability as CS, the proposed approach allows for a six-fold increase in data gathering capability with respect to the baseline CS-based approach. Moreover, under the same energy harvested level, the proposed solution offers a substantial reduction of the mean-squared error distortion (up to 66.67\%) with respect to the state-of-the-art DSC system.

Includes members:
  Member "spatial throughput of mobile ad hoc networks powered by energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "energy cooperation in energy harvesting communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transmission delay minimization in wireless powered communication systems")
  Member "opportunistic wireless energy harvesting in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "design of an miso swipt aided code index modulated multi carrier m dcsk system for e health iot")
  Member "a learning theoretic approach to energy harvesting communication system optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "wireless compressive sensing for energy harvesting sensor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating federated learning via momentum gradient descent")
  Member "towards energy neutrality in energy harvesting wireless sensor networks a case for distributed compressive sensing":
    also connected through other hyperedges:
      (none)
  Member "medium access control protocols for wireless sensor networks with energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proportional fairness in aloha networks with rf energy harvesting")
  Member "enabling wireless power transfer in cellular networks architecture modeling and deployment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cognitive wireless power transfer in the presence of reactive primary communication user")

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)