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 "spectrum access in cognitive radio using a two stage reinforcement learning approach"):
  Abstract: With the advent of the fifth generation of wireless standards and an increasing demand for higher throughput, methods to improve spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, we propose an algorithm to not only select a channel for data transmission, but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized. Our algorithm learns in two stages—a reinforcement learning approach for channel selection and a Bayesian approach to determine the duration for which sensing can be skipped. Comparisons with other methods are provided through extensive simulations. We show that the number of sensing operations is minimized with negligible increase in primary user interference; this implies that less energy is spent by the secondary user in sensing, and also higher throughput is achieved by saving the time spent on sensing.

Includes members:
  Member "a brief introduction to machine learning for engineers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from learning to meta learning reduced training overhead and complexity for communication systems")
  Member "distributed algorithms for learning and cognitive medium access with logarithmic regret":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "algorithms for dynamic spectrum access with learning for cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an active sequential xampling detector for spectrum sensing")

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)