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 "the binary energy harvesting channel with a unit sized battery"):
  Abstract: We consider a binary energy harvesting communication channel with a finite-sized battery at the transmitter. In this model, the channel input is constrained by the available energy at each channel use, which is driven by an external energy harvesting process, the size of the battery, and the previous channel inputs. We consider an abstraction where energy is harvested in binary units and stored in a battery with the capacity of a single unit, and the channel inputs are binary. Viewing the available energy in the battery as a state, this is a state-dependent channel with input-dependent states, memory in the states, and causal state information available at the transmitter only. We find an equivalent representation for this channel based on the timings of the symbols, and determine the capacity of the resulting equivalent timing channel via an auxiliary random variable. We give achievable rates based on certain selections of this auxiliary random variable which resemble lattice coding for the timing channel. We develop upper bounds for the capacity by using a genie-aided method, and also by quantifying the leakage of the state information to the receiver. We show that the proposed achievable rates are asymptotically capacity achieving for small energy harvesting rates. We extend the results to the case of ternary channel inputs. Our achievable rates give the capacity of the binary channel within 0.03 bits/channel use, the ternary channel within 0.05 bits/channel use, and outperform basic Shannon strategies that only consider instantaneous battery states, for all parameter values.

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 "capacity of the awgn channel with random battery recharges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity of remotely powered communication")
  Member "bits through bufferless queues":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stabilizing a linear system using phone calls when time is information")
  Member "interactive joint transfer of energy and information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control")
  Member "binary energy harvesting channel with finite energy storage":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "rf based energy harvesting in decode and forward relaying systems ergodic and outage capacities")

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)