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 "optimal power and rate allocation in the degraded gaussian relay channel with energy harvesting nodes"):
  Abstract: Energy Harvesting (EH) is a novel technique to prolong the lifetime of the wireless networks such as wireless sensor networks or Ad-Hoc networks, by providing an unlimited source of energy for their nodes. In this sense, it has emerged as a promising technique for Green Communications, recently. On the other hand, cooperative communication with the help of relay nodes improves the performance of wireless communication networks by increasing the system throughput or the reliability as well as the range and efficient energy utilization. In order to investigate the cooperation in EH nodes, in this paper, we consider the problem of optimal power and rate allocation in the degraded full-duplex Gaussian relay channel in which source and relay can harvest energy from their environments. We consider the general stochastic energy arrivals at the source and the relay with known EH times and amounts at the transmitters before the start of transmission. This problem has a min-max optimization form that along with the constraints is not easy to solve. We propose a method based on a mathematical theorem proposed by Terkelsen [1] to transform it to a solvable convex optimization form. Also, we consider some special cases for the harvesting profile of the source and the relay nodes and find their solutions efficiently.

Includes members:
  Member "throughput maximization for the gaussian relay channel with energy harvesting constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")
  Member "optimal transmission policies for energy harvesting two hop networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed user association in energy harvesting small cell networks a probabilistic bandit model")
  Member "broadcasting with an energy harvesting rechargeable transmitter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "age of information minimization for an energy harvesting source with updating erasures with and without feedback")
  Member "sum rate optimal power policies for energy harvesting transmitters in an interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "age minimal online policies for energy harvesting sensors with random battery recharges")
  Member "two hop communication with energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")
  Member "transmission with energy harvesting nodes in fading wireless channels optimal policies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "closed form analysis of non linear age of information in status updates with an energy harvesting transmitter")
  Member "the capacity region of classes of product broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "outer bounds on the admissible source region for broadcast channels with correlated sources")
  Member "information capacity of energy harvesting sensor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap")

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)