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: "turbo compressed sensing with partial dft sensing matrix"
  Abstract: In this letter, we propose a turbo compressed sensing algorithm with partial discrete Fourier transform (DFT) sensing matrices. Interestingly, the state evolution of the proposed algorithm is shown to be consistent with that derived using the replica method. Numerical results demonstrate that the proposed algorithm outperforms the well-known approximate message passing (AMP) algorithm when a partial DFT sensing matrix is involved.

Connected through hyperedge HE-1 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting"):
  with members:
    - "vector approximate message passing for the generalized linear model"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "inference from correlated patterns a unified theory for perceptron learning and linear vector channels"

Connected through hyperedge HE-2 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective"):
  with members:
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "expectation propagation for approximate inference free probability framework"
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "expectation consistent approximate inference generalizations and convergence"
    - "vector approximate message passing for the generalized linear model"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  with members:
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "fundamental limits of training based multiuser mimo systems"
    - "channel estimation in massive mimo systems"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "analysis of spectral methods for phase retrieval with random orthogonal matrices"):
  with members:
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "spectral method for phase retrieval an expectation propagation perspective"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "expectation propagation for approximate bayesian inference"
    - "phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-5 (induced by source paper "a unified bayesian inference framework for generalized linear models"):
  with members:
    - "hybrid approximate message passing"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "vector approximate message passing for the generalized linear model"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "denoising based vector approximate message passing"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-6 (induced by source paper "asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions"):
  with members:
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "approximate mmse estimator for linear dynamic systems with gaussian mixture noise"
    - "expectation maximization gaussian mixture approximate message passing"
    - "on the achievability of cramer rao bound in noisy compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"):
  with members:
    - "denoising based turbo compressed sensing"
    - "approximate message passing with nearest neighbor sparsity pattern learning"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "fundamental limits of training based multiuser mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-8 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry"):
  with members:
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "joint spatial division and multiplexing for mm wave channels"
    - "expectation maximization gaussian mixture approximate message passing"

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)