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: "on optimal offline time sharing policy for energy harvesting underlay cognitive radio"
  Abstract: RF energy harvesting can be used to power communication devices so that perpetual operation of such devices can be ensured. We consider a RF energy harvesting underlay cognitive radio system operating in slotted fashion. The primary user (PU) is equipped with a reliable power source and transmits with a constant power in all the slots. However, the secondary user (SU) harvests energy from primary's transmission and simultaneously transmits it's own data such that interference at the primary receiver (PR) remains below an acceptable threshold. At the secondary transmitter (ST), each time slot is divided into two phases: energy harvesting (EH) phase and information transfer (IT) phase. We formulated the problem of maximizing the achievable secondary sum rate under primary receiver's protection criteria as a convex optimization problem and obtained the optimal time sharing between EH and IT phase, and optimal secondary transmit power under offline setting. The optimal offline scheme is then compared with an online myopic policy, where the optimal time sharing between EH and IT phase, and optimal secondary transmit power are obtained based on instantaneous channel gains only.

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 energy cooperation in energy harvesting underlay cognitive radio network"
    - "an optimal transmission policy for energy harvesting systems with continuous energy and data arrivals"
    - "optimal time sharing in underlay cognitive radio systems with rf energy harvesting"
    - "on green energy powered cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "online time sharing policy in energy harvesting cognitive radio network with channel uncertainty"):
  with members:
    - "end to end throughput maximization for underlay multi hop cognitive radio networks with rf energy harvesting"
    - "optimal time sharing in underlay cognitive radio systems with rf energy harvesting"
    - "outage analysis of spectrum sharing energy harvesting cognitive relays in nakagami m channels"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "on green energy powered cognitive radio networks"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "optimal utilization of a cognitive shared channel with a rechargeable primary source node"

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)