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: "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
  Abstract: We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency (RF) chain at the base station (BS) and mobile station (MS) is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival (AoA), angle of departure (AoD), and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e. the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a direct compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

Connected through hyperedge HE-1 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "multi resolution csi feedback with deep learning in massive mimo system"
    - "deep convolutional compression for massive mimo csi feedback"
    - "machine learning in the air"

Connected through hyperedge HE-2 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "faster activity and data detection in massive random access a multi armed bandit approach"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "grant free rateless multiple access a novel massive access scheme for internet of things"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"
    - "low rank matrix completion for mobile edge caching in fog ran via riemannian optimization"

Connected through hyperedge HE-4 (induced by source paper "beam squint and channel estimation for wideband mmwave massive mimo ofdm systems"):
  with members:
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"
    - "beamforming codebook compensation for beam squint with channel capacity constraint"
    - "a new view of multi user hybrid massive mimo non orthogonal angle division multiple access"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "effect of wideband beam squint on codebook design in phased array wireless systems"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"

Connected through hyperedge HE-5 (induced by source paper "matrix calibration based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser mimo"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "passive beamforming and information transfer via large intelligent surface"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"

Connected through hyperedge HE-6 (induced by source paper "matrix completion from quantized samples via generalized sparse bayesian learning"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "fast low rank bayesian matrix completion with hierarchical gaussian prior models"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "categorical matrix completion"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "matrix completion based channel estimation for mmwave communication systems with array inherent impairments"):
  with members:
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network"
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
    - "hybrid analog digital channel estimation and beamforming training throughput tradeoff"

Connected through hyperedge HE-8 (induced by source paper "a two stage approach to estimate cfo and channel with one bit adcs"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "message passing based joint cfo and channel estimation in millimeter wave systems with one bit adcs"
    - "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "millimeter wave cellular wireless networks potentials and challenges"

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)