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 "intelligent wireless communications enabled by cognitive radio and machine learning"):
  Abstract: The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.

Includes members:
  Member "toward intelligent vehicular networks a machine learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "deep learning based channel estimation for beamspace mmwave massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "channel agnostic end to end learning based communication systems with conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wgan based autoencoder training over the air")
  Member "deep learning based csi feedback approach for time varying massive mimo channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "deep reinforcement learning for resource allocation in v2v communications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications")
  Member "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi antenna aided secrecy beamforming optimization for wirelessly powered hetnets")
  Member "deep learning for wireless physical layer opportunities and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")

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)