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 "universally near optimal online power control for energy harvesting nodes"):
  Abstract: We consider online power control for an energy harvesting system with random i.i.d. energy arrivals and a finite size battery. We propose a simple online power control policy for this channel that requires minimal information regarding the distribution of the energy arrivals and prove that it is universally near-optimal for all parameter values. In particular, the policy depends on the distribution of the energy arrival process only through its mean and it achieves the optimal long-term average throughput of the channel within both constant additive and multiplicative gaps. Existing heuristics for online power control fail to achieve such universal performance. This result also allows us to approximate the long-term average throughput of the system with a simple formula, which sheds some light on the qualitative behavior of the throughput, namely how it depends on the distribution of the energy arrivals and the size of the battery.

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 "near optimal energy control and approximate capacity of energy harvesting communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a maximin optimal online power control policy for energy harvesting communications")
  Member "on online energy harvesting in multiple access communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "age minimal transmission for energy harvesting sensors with finite batteries online policies")
  Member "a learning theoretic approach to energy harvesting communication system optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "throughput optimal policies for energy harvesting wireless transmitters with non ideal circuit power":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a maximin optimal online power control policy for energy harvesting communications")
  Member "capacity of fading gaussian channel with an energy harvesting sensor node":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")
  Member "optimal energy allocation for wireless communications with energy harvesting constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems")

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)