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 "harvest or transmit policy for cognitive radio networks a learning theoretic approach"):
  Abstract: We consider an underlay cognitive radio network where the secondary user (SU) harvests energy from the environment. We consider a slotted-mode of operation where each slot of SU is used for either energy harvesting or data transmission. Considering block fading with memory, we model the energy arrival and fading processes as a stationary Markov process of first order. We propose a harvest-or-transmit policy for the SU along with optimal transmit powers that maximize its expected throughput under three different settings. First, we consider a learning-theoretic approach where we do not assume any apriori knowledge about the underlying Markov processes. In this case, we obtain an online policy using Q-learning. Then, we assume that the full statistical knowledge of the governing Markov process is known apriori. Under this assumption, we obtain an optimal online policy using infinite horizon stochastic dynamic programming. Finally, we obtain an optimal offline policy using the generalized Benders decomposition algorithm. The offline policy assumes that for a given time deadline, the energy arrivals and channel states are known in advance at all the transmitters. Finally, we compare all policies and study the effects of various system parameters on the system performance.

Includes members:
  Member "spectrum access in cognitive radio using a two stage reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging online learning for css in frugal iot network")
  Member "optimal harvest or transmit strategy for energy harvesting underlay cognitive radio network":
    also connected through other hyperedges:
      (none)
  Member "end to end throughput maximization for underlay multi hop cognitive radio networks with rf energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous wireless information and power transfer for decode and forward multihop relay systems in energy constrained iot networks")
  Member "online time sharing policy in energy harvesting cognitive radio network with channel uncertainty":
    also connected through other hyperedges:
      (none)
  Member "on optimal offline time sharing policy for energy harvesting underlay cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online time sharing policy in energy harvesting cognitive radio network with channel uncertainty")
  Member "on energy cooperation in energy harvesting underlay cognitive radio network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal harvest or transmit strategy for energy harvesting underlay cognitive radio network")
  Member "an optimal transmission policy for energy harvesting systems with continuous energy and data arrivals":
    also connected through other hyperedges:
      (none)
  Member "optimal time sharing in underlay cognitive radio systems with rf energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sense and predict harnessing spatial interference correlation for cognitive radio networks")

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)