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 "sparse representation for wireless communications"):
  Abstract: Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with focus on the most recent compressive sensing (CS) enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency and energy efficiency for the fifth generation (5G) networks and Internet of Things (IoT) networks. This article starts from a comprehensive overview of CS principles and different sparse domains potentially used in 5G and IoT networks. Then recent research progress on applying CS to address the major opportunities and challenges in 5G and IoT networks is introduced, including wideband spectrum sensing in cognitive radio networks, data collection in IoT networks, and channel estimation and feedback in massive MIMO systems. Moreover, other potential applications and research challenges on sparse representation for 5G and IoT networks are identified. This article will provide readers a clear picture of how to exploit the sparsity properties to process wireless signals in different applications.

Includes members:
  Member "compressive channel estimation and tracking for large arrays in mm wave picocells":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training")
  Member "joint channel training and feedback for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "downlink pilot precoding and compressed channel feedback for fdd based cell free systems")
  Member "compressive sensing based multiuser detector for the large scale sm mimo uplink":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on spatial modulation in emerging wireless systems research progresses and applications")
  Member "compressed sensing for wireless communications useful tips and tricks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems")
  Member "compressed channel feedback for correlated massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning")
  Member "compressive sensing based differential channel feedback for massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for csi feedback based on superimposed coding")
  Member "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation")

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)