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: "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
  Abstract: This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer–Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

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"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"

Connected through hyperedge HE-2 (induced by source paper "data driven deep learning to design pilot and channel estimator for massive mimo"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
    - "deep learning in physical layer communications"
    - "compressive sensing techniques for next generation wireless communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-3 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "compressive sensing techniques for next generation wireless communications"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "compressed sensing for wireless communications useful tips and tricks"

Connected through hyperedge HE-4 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "device to device communications enabled energy efficient multicast scheduling in mmwave small cells"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "massive mimo channel estimation for millimeter wave systems via matrix completion"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"

Connected through hyperedge HE-5 (induced by source paper "deep learning for csi feedback based on superimposed coding"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "learning to detect"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "deep learning in physical layer communications"
    - "mimo channel information feedback using deep recurrent network"
    - "multi task learning as multi objective optimization"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"

Connected through hyperedge HE-6 (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"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "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-7 (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"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "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"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"

Connected through hyperedge HE-8 (induced by source paper "time varying downlink channel tracking for quantized massive mimo networks"):
  with members:
    - "beamspace aware adaptive channel estimation for single carrier time varying massive mimo channels"
    - "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks"
    - "bayesian optimal data detector for mmwave ofdm system with low resolution adc"
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"

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)