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 "resource allocation based on deep neural networks for cognitive radio networks"):
  Abstract: Resource allocation is of great importance in the next generation wireless communication systems, especially for cognitive radio networks (CRNs). Many resource allocation strategies have been proposed to optimize the performance of CRNs. However, it is challenging to implement these strategies and achieve real-time performance in wireless systems since most of them need accurate and timely channel state information and/or other network statistics. In this paper a resource allocation strategy based on deep neural networks (DNN) is proposed and the training method is presented to train the neural networks. Simulation results show that our proposed strategy based on DNN is efficient in terms of the computation time compared with the conventional resource allocation schemes.

Includes members:
  Member "echo state networks for proactive caching in cloud based radio access networks with mobile users":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "why deep neural networks for function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "an overview of gradient descent optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal power control for over the air computation in fading channels")

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)