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: "an optimal transmission policy for energy harvesting systems with continuous energy and data arrivals"
  Abstract: Energy harvesting has been developed as an effective technology for communication systems in order to extend the lifetime of these systems. In this work, we consider a singleuser energy harvesting wireless communication system, in which arrival data and harvested energy curves are modeled as continuous functions. For the single-user model, our first goal is to find an offline algorithm, which maximizes the amount of data which is transmitted to the receiver node by a given deadline. If more than one scheme exists that transmits the maximum data, we choose the one with minimum utilized energy at the transmitter node. Next, we propose an online algorithm for this system. We also consider a multi-hop energy harvesting wireless communication system in a full-duplex mode and find the optimal offline algorithm to maximize the throughput.

Connected through hyperedge HE-1 (induced by source paper "harvest or transmit policy for cognitive radio networks a learning theoretic approach"):
  with members:
    - "spectrum access in cognitive radio using a two stage reinforcement learning approach"
    - "optimal harvest or transmit strategy for energy harvesting underlay cognitive radio network"
    - "end to end throughput maximization for underlay multi hop cognitive radio networks with rf energy harvesting"
    - "online time sharing policy in energy harvesting cognitive radio network with channel uncertainty"
    - "on optimal offline time sharing policy for energy harvesting underlay cognitive radio"
    - "on energy cooperation in energy harvesting underlay cognitive radio network"
    - "optimal time sharing in underlay cognitive radio systems with rf energy harvesting"
    - "on green energy powered cognitive radio networks"

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)