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: "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
  Abstract: In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing uplink/downlink channel covariance matrices (CCMs) with the aid of array signal processing techniques. Specifically, the angle information and power angular spectrum (PAS) of each multi-path channel is extracted from the instantaneous uplink channel state information (CSI). Then, the uplink CCM is reconstructed and can be used to improve the uplink channel estimation without any additional training cost. In virtue of angle reciprocity as well as PAS reciprocity between uplink and downlink channels, the downlink CCM could also be inferred with a similar approach even for FDD massive MIMO systems. Then, the downlink instantaneous CSI can be obtained by training towards the dominant eigen-directions of each user. The proposed strategy is applicable for any kind of PAS distributions and array geometries. Numerical results are provided to demonstrate the superiority of the proposed methods over the existing ones.

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"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-2 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection"):
  with members:
    - "semi blind channel and signal estimation for uplink massive mimo with channel sparsity"
    - "multi band covariance interpolation with applications in massive mimo"
    - "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "massive mimo for next generation wireless systems"
    - "statistical beamforming on the grassmann manifold for the two user broadcast channel"

Connected through hyperedge HE-3 (induced by source paper "deep transfer learning based downlink channel prediction for fdd massive mimo systems"):
  with members:
    - "fdd massive mimo based on efficient downlink channel reconstruction"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "decaying momentum helps neural network training"
    - "learning to transfer unsupervised meta domain translation"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "model aided deep neural network for source number detection"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"

Connected through hyperedge HE-4 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems"):
  with members:
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "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"
    - "enabling covariance based feedback in massive mimo a user classification approach"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"

Connected through hyperedge HE-5 (induced by source paper "an efficient beam and channel acquisition via sparsity map and joint angle delay power profile estimation for wideband massive mimo systems"):
  with members:
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "enhancing the estimation of mm wave large array channels by exploiting spatio temporal correlation and sparse scattering"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "a generalized framework on beamformer design and csi acquisition for single carrier massive mimo systems in millimeter wave channels"
    - "hybrid beamforming for massive mimo a survey"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"

Connected through hyperedge HE-6 (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"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "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"
    - "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-7 (induced by source paper "time varying massive mimo channel estimation capturing reconstruction and restoration"):
  with members:
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "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-8 (induced by source paper "clustering based codebook design for mimo communication system"):
  with members:
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep learning for massive mimo csi feedback"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "ensemble properties of rvq based limited feedback beamforming codebooks"

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)