human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "efficient downlink channel reconstruction for fdd multi antenna systems"
  Abstract: In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. We first estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high estimation accuracy. Simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting that the promising application of the proposed scheme in large-scale antenna array systems.

Connected through hyperedge HE-1 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "non stationarities in extra large scale massive mimo"
    - "multi band covariance interpolation with applications in massive mimo"
    - "yolov3 an incremental improvement"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-2 (induced by source paper "fdd massive mimo based on efficient downlink channel reconstruction"):
  with members:
    - "dft based hybrid beamforming multiuser systems rate analysis and beam selection"
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-3 (induced by source paper "time varying massive mimo channel estimation capturing reconstruction and restoration"):
  with members:
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "design and implementation of a tdd based 128 antenna massive mimo prototyping system"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"

Connected through hyperedge HE-4 (induced by source paper "multidimensional variational line spectra estimation"):
  with members:
    - "grid less variational bayesian inference of line spectral from quantized samples"
    - "variational bayesian inference of line spectra"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-5 (induced by source paper "full downlink channel reconstruction using incomplete uplink channel measurements in massive mimo networks"):
  with members:
    - "position and orientation estimation through millimeter wave mimo in 5g systems"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-6 (induced by source paper "fdd massive mimo uplink and downlink channel reciprocity properties full or partial reciprocity"):
  with members:
    - "channel extrapolation in fdd massive mimo theoretical analysis and numerical validation"

Connected through hyperedge HE-7 (induced by source paper "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Connected through hyperedge HE-8 (induced by source paper "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Question: Please tell me which class the center node 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)