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 "joint retransmission compression and channel coding for data fidelity under energy constraints"):
  Abstract: We consider a monitoring application where sensors periodically report data to a common receiver in a time division multiplex fashion. The sensors are constrained by the limited and unpredictable energy availability provided by Energy Harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to $r - 1$ previously unsuccessfully delivered ones, where $r$ is a design parameter; such blocks are compressed, concatenated and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of $r$, the compression and coding rates, under the constraints of the energy availability, and, once $r$ has been decided, use a Markov Decision Process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.

Includes members:
  Member "reconstruction of correlated sources with energy harvesting constraints in delay constrained and delay tolerant communication scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic routing and scheduling policies for energy harvesting communication networks")
  Member "energy harvesting wireless communications a review of recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "data driven stochastic models and policies for energy harvesting sensor communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication over a time correlated channel with an energy harvesting transmitter")
  Member "solarstat modeling photovoltaic sources through stochastic markov processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy sustainable paradigms and methods for future mobile networks a survey")
  Member "secure layered transmission in multicast systems with wireless information and power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure beamforming for mimo broadcasting with wireless information and power transfer")
  Member "a learning theoretic approach to energy harvesting communication system optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "energy management policies for energy neutral source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic routing and scheduling policies for energy harvesting communication 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)