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: "convexity in source separation models geometry and algorithms"
  Abstract: Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, blind deconvolution, and even dictionary learning. Despite the recent progress in each of these applications, advances in high-throughput sensor technology place demixing algorithms under pressure to accommodate extremely high-dimensional signals, separate an ever larger number of sources, and cope with more sophisticated signal and mixing models. These difficulties are exacerbated by the need for real-time action in automated decision-making systems.

Connected through hyperedge HE-1 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-2 (induced by source paper "painless breakups efficient demixing of low rank matrices"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "convolutional dictionary learning through tensor factorization"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "sparse signal processing concepts for efficient 5g system design"
    - "low rank modeling and its applications in image analysis"
    - "the achievable performance of convex demixing"
    - "blind deconvolution using convex programming"
    - "compressive principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "fast singular value shrinkage with chebyshev polynomial approximation based on signal sparsity"):
  with members:
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "an algorithm for improving non local means operators via low rank approximation"
    - "low rank modeling and its applications in image analysis"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"):
  with members:
    - "positive definite estimation of large covariance matrix using generalized nonconvex penalties"
    - "efficient algorithms for robust recovery of images from compressed data"
    - "stable restoration and separation of approximately sparse signals"
    - "recovery of sparsely corrupted signals"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-5 (induced by source paper "precise error analysis of regularized m estimators in high dimensions"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "the generalized lasso with non linear observations"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-6 (induced by source paper "beyond low rank sparse multiscale low rank matrix decomposition"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the achievable performance of convex demixing"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "phase retrieval via matrix completion"
    - "robust pca via outlier pursuit"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "blind deconvolution meets blind demixing algorithms and performance bounds"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "self calibration and biconvex compressive sensing"
    - "sparse signal processing concepts for efficient 5g system design"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-8 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "convex relaxations of structured matrix factorizations"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"
    - "convex approaches to model wavelet sparsity patterns"

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)