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: "near optimal compressed sensing of a class of sparse low rank matrices via sparse power factorization"
  Abstract: Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. One important question is how many measurements are needed for a stable reconstruction in the presence of measurement noise. Unlike conventional compressed sensing for sparse vectors, where convex relaxation via the $\ell_1$-norm achieves near optimal performance, for compressed sensing of sparse low-rank matrices, it has been shown recently Oymak et al. that convex programmings using the nuclear norm and the mixed norm are highly suboptimal even in the noise-free scenario. #R##N#We propose an alternating minimization algorithm called sparse power factorization (SPF) for compressed sensing of sparse rank-one matrices. For a class of signals whose sparse representation coefficients are fast-decaying, SPF achieves stable recovery of the rank-1 matrix formed by their outer product and requires number of measurements within a logarithmic factor of the information-theoretic fundamental limit. For the recovery of general sparse low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has analogous near optimal performance guarantees to SPF in the rank-1 case. Numerical results show that SPF and SCSPF empirically outperform convex programmings using the best known combinations of mixed norm and nuclear norm.

Connected through hyperedge HE-1 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "provable subspace tracking from missing data and matrix completion"
    - "phaseless pca low rank matrix recovery from column wise phaseless measurements"
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"

Connected through hyperedge HE-2 (induced by source paper "the non convex geometry of low rank matrix optimization"):
  with members:
    - "global optimality in low rank matrix optimization"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "the global optimization geometry of low rank matrix optimization"
    - "overcomplete tensor decomposition via convex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"

Connected through hyperedge HE-3 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"

Connected through hyperedge HE-4 (induced by source paper "sparse power factorization balancing peakiness and sample complexity"):
  with members:
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "solving equations of random convex functions via anchored regression"
    - "blind demixing and deconvolution at near optimal rate"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"

Connected through hyperedge HE-5 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-6 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  with members:
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-7 (induced by source paper "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "non convex phase retrieval from stft measurements"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "fast algorithms for robust pca via gradient descent"
    - "phase retrieval using gauss newton method"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-8 (induced by source paper "blind gain and phase calibration via sparse spectral methods"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "sensor calibration for off the grid spectral estimation"
    - "from blind deconvolution to blind super resolution through convex programming"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and bilinear inverse problems via linear least squares"

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)