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: "online power control for block i i d energy harvesting channels"
  Abstract: We study the problem of online power control for energy harvesting communication nodes with random energy arrivals and a finite battery. We assume a block i.i.d. stochastic model for the energy arrivals, in which the energy arrivals are constant for a fixed duration $T$, but are independent across different blocks, drawn from an arbitrary distribution. This model serves as a simple approximation to a random process with coherence time $T$. We propose a simple online power control policy, and prove that its performance gap to the optimal throughput is bounded by a constant which is independent of the parameters of the problem. This also yields a simple formula for the approximately optimal long-term average throughput, which sheds some light on the qualitative behavior of the throughput and how it depends on the coherence time of the energy arrival process. Our results show that, perhaps counter-intuitively, for a fixed mean energy arrival rate the throughput decreases with increasing coherence time $T$ of the energy arrival process. In particular, the battery size needed to approach the AWGN capacity of the channel increases linearly with the coherence time of the process. Finally, we show that our results can provide an approximation to the information-theoretic capacity of the same channel.

Connected through hyperedge HE-1 (induced by source paper "age minimal transmission for energy harvesting sensors with finite batteries online policies"):
  with members:
    - "using erasure feedback for online timely updating with an energy harvesting sensor"
    - "online timely status updates with erasures for energy harvesting sensors"
    - "optimal status updating for an energy harvesting sensor with a noisy channel"
    - "coded status updates in an energy harvesting erasure channel"
    - "status updates in a multi stream m g 1 1 preemptive queue"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "sending information through status updates"
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"

Connected through hyperedge HE-2 (induced by source paper "non asymptotic achievable rates for gaussian energy harvesting channels best effort and save and transmit"):
  with members:
    - "on achievable rates of awgn energy harvesting channels with block energy arrival and non vanishing error probabilities"
    - "capacity analysis of discrete energy harvesting channels"
    - "finite blocklength achievable rates for energy harvesting awgn channels with infinite buffer"
    - "non asymptotic achievable rates for energy harvesting channels using save and transmit"
    - "capacity of the energy harvesting channel with a finite battery"
    - "closed form delay optimal power control for energy harvesting wireless system with finite energy storage"
    - "the binary energy harvesting channel with a unit sized battery"

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)