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 "enhancing the estimation of mm wave large array channels by exploiting spatio temporal correlation and sparse scattering"):
  Abstract: In order to cope with the large path-loss exponent of mm-Wave channels, a high beamforming gain is needed. This can be achieved with small hardware complexity and high hardware power efficiency by Hybrid Digital-Analog (HDA) beamforming, where a very large number $M\gg 1$ of antenna array elements requires only a relatively small $m\ll M$ number of A/D converters and modulators/demodulators. As such, the estimation of mm-Wave MIMO channels must deal with two specific problems: 1) high Doppler, due to the large carrier frequency; 2) impossibility of observing directly the M-dimensional channel vector at the antenna array elements, due to the mentioned HDA implementation. In this paper, we consider a novel scheme inspired by recent results on gridless multiple measurement vectors problem in compressed sensing, that is able to exploit the inherent mm-Wave channel sparsity in the angular domain in order to cope with both the above problems simultaneously. Our scheme uses past pilot-symbol observations in a window of length $T$ in order to estimate a low-dimensional subspace that approximately contains the channel vector at the current time. This subspace information can be used directly, in order to separate users in the spatial domain, or indirectly, in order to improve the estimate of the user channel vector from the current pilot-symbol observation.

Includes members:
  Member "channel vector subspace estimation from low dimensional projections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification")
  Member "off the grid line spectrum denoising and estimation with multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "joint spatial division and multiplexing for mm wave channels":
    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 "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "subspace methods for joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")

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)