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 "dictionary learning for channel estimation in hybrid frequency selective mmwave mimo systems"):
  Abstract: Exploiting channel sparsity at millimeter wave (mmWave) frequencies reduces the high training overhead associated with the channel estimation stage. Compressive sensing (CS) channel estimation techniques usually adopt the (overcomplete) wavelet/Fourier transform matrix as a sparsifying dictionary. This may not be the best choice when considering non-uniform arrays, antenna gain/phase errors, mutual coupling effects, etc. We propose two dictionary learning (DL) algorithms to learn the best sparsifying dictionaries for channel matrices from observations obtained with hybrid frequency-selective mmWave multiple-input-multiple-output (MIMO) systems. First, we optimize the combined dictionary, i.e., the Kronecker product of transmit and receive dictionaries, as it is used in practice to sparsify the channel matrix. Second, considering the different array structures at the transmitter and receiver, we exploit separable DL to find the best transmit and receive dictionaries. Once the channel is expressed in terms of the optimized dictionaries, various CS-based sparse recovery techniques can be applied for low overhead channel estimation. The proposed DL algorithms perform well under low SNR conditions inherent to any mmWave communication systems before the precoders/combiners can be optimized. The effectiveness of the proposed DL algorithms has been corroborated via numerical simulations with different system configurations, array geometries and hardware impairments.

Includes members:
  Member "spatial and frequency wideband effects in millimeter wave massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems")
  Member "an overview of signal processing techniques for millimeter wave mimo 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 "channel estimation for hybrid architecture based wideband millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection")
  Member "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "novel methods for multilinear data completion and de noising based on tensor svd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low rank tensor grid for image completion")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")

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)