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 sparse vector recovery performance in structurally orthogonal matrices via lasso"
  Abstract: In this paper, we consider a compressed sensing problem of reconstructing a sparse signal from an undersampled set of noisy linear measurements. The regularized least squares or least absolute shrinkage and selection operator (LASSO) formulation is used for signal estimation. The measurement matrix is assumed to be constructed by concatenating several randomly orthogonal bases, referred to as structurally orthogonal matrices. Such measurement matrix is highly relevant to large-scale compressive sensing applications because it facilitates fast computation and also supports parallel processing. Using the replica method from statistical physics, we derive the mean-squared-error (MSE) formula of reconstruction over the structurally orthogonal matrix in the large-system regime. Extensive numerical experiments are provided to verify the analytical result. We then use the analytical result to study the MSE behaviors of LASSO over the structurally orthogonal matrix, with a particular focus on performance comparisons to matrices with independent and identically distributed (i.i.d.) Gaussian entries. We demonstrate that the structurally orthogonal matrices are at least as well performed as their i.i.d. Gaussian counterparts, and therefore the use of structurally orthogonal matrices is highly motivated in practical applications.

Connected through hyperedge HE-1 (induced by source paper "sparse vector recovery bernoulli gaussian message passing"):
  with members:
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "expectation maximization gaussian mixture approximate message passing"
    - "support recovery with sparsely sampled free random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"

Connected through hyperedge HE-2 (induced by source paper "statistical mechanics of map estimation general replica ansatz"):
  with members:
    - "glse precoders for massive mimo systems analysis and applications"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "replica symmetry breaking in compressive sensing"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"

Connected through hyperedge HE-3 (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"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "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"
    - "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-4 (induced by source paper "on the performance of turbo signal recovery with partial dft sensing matrices"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "compressive imaging via approximate message passing with image denoising"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "analysis of compressed sensing with spatially coupled orthogonal matrices"
    - "s amp approximate message passing for general matrix ensembles"
    - "near optimal compressed sensing without priors parametric sure approximate message passing"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "a tight version of the gaussian min max theorem in the presence of convexity"
    - "the gaussian min max theorem in the presence of convexity"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "optimal phase transitions in compressed sensing"

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)