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: "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
  Abstract: Spatial channel covariance information can replace full knowledge of the entire channel matrix for designing analog precoders in hybrid multiple-input-multiple-output (MIMO) architecture. Spatial channel covariance estimation, however, is challenging for the hybrid MIMO architecture because the estimator operating at baseband can only obtain a lower dimensional pre-combined signal through fewer radio frequency (RF) chains than antennas. In this paper, we propose two approaches for covariance estimation based on compressive sensing techniques. One is to apply a time-varying sensing matrix, and the other is to exploit the prior knowledge that the covariance matrix is Hermitian. We present the rationale of the two ideas and validate the superiority of the proposed methods by theoretical analysis and numerical simulations. We conclude the paper by extending the proposed algorithms from narrowband massive MIMO systems with a single receive antenna to wideband systems with multiple receive antennas.

Connected through hyperedge HE-1 (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:
    - "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"
    - "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-2 (induced by source paper "passive radar at the roadside unit to configure millimeter wave vehicle to infrastructure links"):
  with members:
    - "an approximate message passing framework for side information"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "millimeter wave beam selection using out of band spatial information"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-3 (induced by source paper "user scheduling for millimeter wave hybrid beamforming systems with low resolution adcs"):
  with members:
    - "cellular connected uav potentials challenges and promising technologies"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "user scheduling for millimeter wave mimo communications with low resolution adcs"
    - "resolution adaptive hybrid mimo architectures for millimeter wave communications"
    - "adc bit optimization for spectrum and energy efficient millimeter wave communications"
    - "codebook based hybrid precoding for millimeter wave multiuser systems"
    - "narrowband channel estimation for hybrid beamforming millimeter wave communication systems with one bit quantization"

Connected through hyperedge HE-4 (induced by source paper "spatial covariance estimation for millimeter wave hybrid systems using out of band information"):
  with members:
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave beam selection using out of band spatial information"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-5 (induced by source paper "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"):
  with members:
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "learning what and where to draw"
    - "generative adversarial text to image synthesis"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "conditional generative adversarial nets"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "joint spatial division and multiplexing for mm wave channels"

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)