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 feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
  Abstract: Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

Connected through hyperedge HE-1 (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"
    - "enabling covariance based feedback in massive mimo a user classification approach"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "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-2 (induced by source paper "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems"):
  with members:
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems"
    - "mixed adc massive mimo detectors performance analysis and design optimization"

Connected through hyperedge HE-3 (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 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-4 (induced by source paper "bit level optimized neural network for multi antenna channel quantization"):
  with members:
    - "mimo channel information feedback using deep recurrent network"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "lstm a search space odyssey"
    - "deep learning based mimo communications"
    - "deep learning for massive mimo csi feedback"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "downlink pilot precoding and compressed channel feedback for fdd based cell free systems"):
  with members:
    - "on the total energy efficiency of cell free massive mimo"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "cell free massive mimo versus small cells"
    - "power allocation schemes for multicell massive mimo systems"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-6 (induced by source paper "efficient downlink channel reconstruction for fdd multi antenna systems"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "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"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-7 (induced by source paper "joint spatial division and diversity for massive mimo systems"):
  with members:
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-8 (induced by source paper "mimo channel information feedback using deep recurrent network"):
  with members:
    - "an enhanced scma detector enabled by deep neural network"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "lstm a search space odyssey"
    - "deep learning for massive mimo csi feedback"
    - "deep residual learning for image recognition"

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)