human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "capacity of the awgn channel with random battery recharges"
  Abstract: We consider communication over the AWGN channel with a transmitter whose battery is recharged with RF energy transfer at random times known to the receiver. We assume that the recharging process is i.i.d. Bernoulli. We characterize the capacity of this channel as the limit of an $n$-letter maximum mutual information rate under both causal and noncausal transmitter knowledge of the battery recharges. With noncausal knowledge, it is possible to explicitly identify the maximizing input distribution, which we use to demonstrate that the capacity with noncausal knowledge of the battery recharges is strictly larger than that with causal knowledge. We then proceed to derive explicit upper and lower bounds on the capacity, which are within 1.05 bits/s/Hz of each other for all parameter values.

Connected through hyperedge HE-1 (induced by source paper "capacity of remotely powered communication"):
  with members:
    - "asymptotics of input constrained erasure channel capacity"
    - "capacity of the energy harvesting channel with a finite battery"
    - "energy harvesting wireless communications a review of recent advances"
    - "can feedback increase the capacity of the energy harvesting channel"
    - "universally near optimal online power control for energy harvesting nodes"
    - "subblock constrained codes for real time simultaneous energy and information transfer"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "constrained codes for joint energy and information transfer"

Connected through hyperedge HE-2 (induced by source paper "non asymptotic achievable rates for energy harvesting channels using save and transmit"):
  with members:
    - "finite blocklength achievable rates for energy harvesting awgn channels with infinite buffer"
    - "capacity of the energy harvesting channel with a finite battery"
    - "energy harvesting wireless communications a review of recent advances"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "the third order term in the normal approximation for the awgn channel"
    - "channels with cost constraints strong converse and dispersion"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "binary energy harvesting channel with finite energy storage"

Connected through hyperedge HE-3 (induced by source paper "capacity of the energy harvesting gaussian mac"):
  with members:
    - "capacity of the energy harvesting channel with a finite battery"
    - "universally near optimal online power control for energy harvesting nodes"
    - "on online energy harvesting in multiple access communication systems"
    - "binary energy harvesting channel with finite energy storage"
    - "awgn channel under time varying amplitude constraints with causal information at the transmitter"

Connected through hyperedge HE-4 (induced by source paper "capacity of the energy harvesting channel with a finite battery"):
  with members:
    - "can feedback increase the capacity of the energy harvesting channel"
    - "universally near optimal online power control for energy harvesting nodes"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "on online energy harvesting in multiple access communication systems"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "binary energy harvesting channel with finite energy storage"
    - "capacity of fading gaussian channel with an energy harvesting sensor node"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-5 (induced by source paper "universally near optimal online power control for energy harvesting nodes"):
  with members:
    - "capacity of the energy harvesting channel with a finite battery"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "on online energy harvesting in multiple access communication systems"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "throughput optimal policies for energy harvesting wireless transmitters with non ideal circuit power"
    - "capacity of fading gaussian channel with an energy harvesting sensor node"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-6 (induced by source paper "the binary energy harvesting channel with a unit sized battery"):
  with members:
    - "capacity of the energy harvesting channel with a finite battery"
    - "bits through bufferless queues"
    - "interactive joint transfer of energy and information"
    - "binary energy harvesting channel with finite energy storage"
    - "awgn channel under time varying amplitude constraints with causal information at the transmitter"

Question: Please tell me which class the center node 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)