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: "sparse modeling for image and vision processing"
  Abstract: In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "controlling neural networks via energy dissipation"):
  with members:
    - "linear inequality constraints for neural network activations"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "homogeneous linear inequality constraints for neural network activations"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "scale recurrent network for deep image deblurring"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep image prior"
    - "recurrent relational networks"

Connected through hyperedge HE-3 (induced by source paper "visual diagnosis of dermatological disorders human and machine performance"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "diagnostic accuracy of content based dermatoscopic image retrieval with deep classification features"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "knowledge transfer for melanoma screening with deep learning"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "compact bilinear pooling"

Connected through hyperedge HE-4 (induced by source paper "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "subgradient descent learns orthogonal dictionaries"
    - "a convergence theory for deep learning via over parameterization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast and robust tensor decomposition with applications to dictionary learning"

Connected through hyperedge HE-5 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "provable self representation based outlier detection in a union of subspaces"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "greedy feature selection for subspace clustering"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

Connected through hyperedge HE-6 (induced by source paper "large scale sparse subspace clustering using landmarks"):
  with members:
    - "efficient solvers for sparse subspace clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "invariant scattering convolution networks"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "part based approximations for morphological operators using asymmetric auto encoders"):
  with members:
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep learning of part based representation of data using sparse autoencoders with nonnegativity constraints"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to generate chairs with convolutional neural networks"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-8 (induced by source paper "on the global geometry of sphere constrained sparse blind deconvolution"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "complete dictionary recovery over the sphere"
    - "revisiting bayesian blind deconvolution"
    - "blind deconvolution using convex programming"
    - "exact recovery of sparsely used dictionaries"

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.CV(Computer Vision and Pattern Recognition)