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: "on the performance of turbo signal recovery with partial dft sensing matrices"
  Abstract: This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.

Connected through hyperedge HE-1 (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"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "bayesian optimal data detector for mmwave ofdm system with low resolution adc"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "channel estimation and data equalization in frequency selective mimo systems with one bit quantization"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "mixed adc massive mimo uplink in frequency selective channels"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "unveiling bias compensation in turbo based algorithms for discrete compressed sensing"):
  with members:
    - "low complexity iterative algorithms for discrete compressed sensing"
    - "algorithms for the iterative estimation of discrete valued sparse vectors"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "non linear transformations of gaussians and gaussian mixtures with implications on estimation and information theory"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "low complexity iterative algorithms for discrete compressed sensing"):
  with members:
    - "algorithms for the iterative estimation of discrete valued sparse vectors"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Connected through hyperedge HE-5 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"):
  with members:
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "statistical mechanics approach to sparse noise denoising"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-6 (induced by source paper "on sparse vector recovery performance in structurally orthogonal matrices via lasso"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "multi processor approximate message passing using lossy compression"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "analysis of compressed sensing with spatially coupled orthogonal matrices"

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)