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 sparse rank one matrices via sparse power factorization"
  Abstract: Compressed sensing of simultaneously sparse and rank-one 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 the conventional compressed sensing for sparse vectors, where convex relaxation via the $\ell_1$-norm achieves near optimal performance, for compressed sensing of sparse and rank-one matrices, recently it has been shown by 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. Starting from a particular initialization, SPF achieves stable recovery and requires number of measurements within a logarithmic factor of the information-theoretic fundamental limit. For fast-decaying sparse signals, SPF starting from an initialization with low computational cost also achieves stable reconstruction with the same number of measurements. Numerical results show that SPF empirically outperforms the best known combinations of mixed norm and nuclear norm.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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"

Connected through hyperedge HE-5 (induced by source paper "an overview of low rank matrix recovery from incomplete observations"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "poisson matrix recovery and completion"
    - "self calibration and biconvex compressive sensing"
    - "collaboratively learning preferences from ordinal data"
    - "categorical matrix completion"
    - "incoherence optimal matrix completion"

Connected through hyperedge HE-6 (induced by source paper "a geometric analysis of phase retrieval"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "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"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "phase retrieval via incremental truncated wirtinger flow"
    - "phaseless rcovery using gauss newton method"

Connected through hyperedge HE-7 (induced by source paper "super resolution of complex exponentials from modulations with unknown waveforms"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "self calibration and biconvex compressive sensing"
    - "super resolution of positive sources the discrete setup"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"

Connected through hyperedge HE-8 (induced by source paper "empirical chaos processes and blind deconvolution"):
  with members:
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "rip like properties in subsampled blind deconvolution"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "identifiability and stability in blind deconvolution under minimal assumptions"

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)