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 "capacity analysis of discrete energy harvesting channels"):
  Abstract: We study the channel capacity of a general discrete energy harvesting channel with a finite battery. Contrary to traditional communication systems, the transmitter of such a channel is powered by a device that harvests energy from a random exogenous energy source and has a finite-sized battery. As a consequence, at each transmission opportunity the system can only transmit a symbol whose energy is no more than the energy currently available. This new type of power supply introduces an unprecedented input constraint for the channel, which is simultaneously random, instantaneous, and influenced by the full history of the inputs and the energy harvesting process. Furthermore, naturally, in such a channel the energy information is observed causally at the transmitter. Both of these characteristics pose great challenges for the analysis of the channel capacity. In this work we use techniques developed for channels with side information and finite state channels, to obtain lower and upper bounds on the capacity of energy harvesting channels. In particular, in a general case with Markov energy harvesting processes we use stationarity and ergodicity theory to compute and optimize the achievable rates for the channels, and derive series of computable capacity upper and lower bounds.

Includes members:
  Member "capacity of the energy harvesting channel with a finite battery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non asymptotic achievable rates for gaussian energy harvesting channels best effort and save and transmit")
  Member "can feedback increase the capacity of the energy harvesting channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feedback capacity and coding for the bibo channel with a no repeated ones input constraint")
  Member "capacity of a post channel with and without feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "a randomized approach to the capacity of finite state channels":
    also connected through other hyperedges:
      (none)
  Member "binary energy harvesting channel with finite energy storage":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint transfer of energy and information in a two hop relay channel")
  Member "awgn channel under time varying amplitude constraints with causal information at the transmitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rf based energy harvesting in decode and forward relaying systems ergodic and outage capacities")
  Member "optimum transmission policies for battery limited energy harvesting nodes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")
  Member "optimal packet scheduling in an energy harvesting communication system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")

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)