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 "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"):
  Abstract: We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e. MSs) to the BS. The proposed method exploits the sparse scattering nature of the mmWave channel and the intrinsic multi-dimensional structure of the multiway data collected from multiple modes. The uniqueness of the CP decomposition is studied and sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by employing the layered pilot transmission scheme. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.

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 "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "statistical blockage modeling and robustness of beamforming in millimeter wave systems")
  Member "limited feedback hybrid precoding for multi user millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "compressed sensing based multi user millimeter wave systems how many measurements are needed":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "tensor decompositions for signal processing applications from two way to multiway component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tensor decompositions in deep learning")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "millimeter wave cellular wireless networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "spatially sparse precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")

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)