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: "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"
  Abstract: We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the BS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP decomposition, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramer-Rao bound (CRB) results for channel parameters, and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs, and presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.

Connected through hyperedge HE-1 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-2 (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"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deep cnn based channel estimation for mmwave massive mimo systems"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"

Connected through hyperedge HE-4 (induced by source paper "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"):
  with members:
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"

Connected through hyperedge HE-5 (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"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "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-6 (induced by source paper "a hardware efficient analog network structure for hybrid precoding in millimeter wave systems"):
  with members:
    - "hybrid precoding in millimeter wave systems how many phase shifters are needed"
    - "hybrid analog and digital beamforming for mmwave ofdm large scale antenna arrays"
    - "alternating minimization for hybrid precoding in multiuser ofdm mmwave systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "multi layer precoding a potential solution for full dimensional massive mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"

Connected through hyperedge HE-7 (induced by source paper "fast beam alignment for millimeter wave communications a sparse encoding and phaseless decoding approach"):
  with members:
    - "efficient beam alignment for mmwave single carrier systems with hybrid mimo transceivers"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "adaptive millimeter wave beam alignment for dual polarized mimo systems"

Connected through hyperedge HE-8 (induced by source paper "millimeter wave channel estimation via exploiting joint sparse and low rank structures"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "efficient compressive phase retrieval with constrained sensing vectors"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

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)