human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "sparsity based poisson denoising with dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "sparse and spurious dictionary learning with noise and outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "generalized max pooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained visual textual representation learning")
  Member "fast and robust archetypal analysis for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrete laplace operator estimation for dynamic 3d reconstruction")
  Member "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "learning sparsely used overcomplete dictionaries via alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks")

Question: Please tell me which class the source paper 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)