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: "dictionary learning for deblurring and digital zoom"
  Abstract: This paper proposes a novel approach to image deblurring and digital zooming using sparse local models of image appearance. These models, where small image patches are represented as linear combinations of a few elements drawn from some large set (dictionary) of candidates, have proven well adapted to several image restoration tasks. A key to their success has been to learn dictionaries adapted to the reconstruction of small image patches. In contrast, recent works have proposed instead to learn dictionaries which are not only adapted to data reconstruction, but also tuned for a specific task. We introduce here such an approach to deblurring and digital zoom, using pairs of blurry/sharp (or low-/high-resolution) images for training, as well as an effective stochastic gradient algorithm for solving the corresponding optimization task. Although this learning problem is not convex, once the dictionaries have been learned, the sharp/high-resolution image can be recovered via convex optimization at test time. Experiments with synthetic and real data demonstrate the effectiveness of the proposed approach, leading to state-of-the-art performance for non-blind image deblurring and digital zoom.

Connected through hyperedge HE-1 (induced by source paper "supervised convolutional sparse coding"):
  with members:
    - "cross label suppression a discriminative and fast dictionary learning with group regularization"
    - "optimization methods for convolutional sparse coding"

Connected through hyperedge HE-2 (induced by source paper "fast convolutional sparse coding in the dual domain"):
  with members:
    - "optimization methods for convolutional sparse coding"
    - "fast stochastic alternating direction method of multipliers"

Connected through hyperedge HE-3 (induced by source paper "fast orthonormal sparsifying transforms based on householder reflectors"):
  with members:
    - "on the computational intractability of exact and approximate dictionary learning"
    - "more algorithms for provable dictionary learning"
    - "fast approximation of rotations and hessians matrices"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "learning computationally efficient dictionaries and their implementation as fast transforms"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-4 (induced by source paper "on the computational intractability of exact and approximate dictionary learning"):
  with members:
    - "more algorithms for provable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"
    - "matrix sparsification and the sparse null space problem"

Connected through hyperedge HE-5 (induced by source paper "sparse modeling for image and vision processing"):
  with members:
    - "sparsity based poisson denoising with dictionary learning"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "generalized max pooling"
    - "fast and robust archetypal analysis for representation learning"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "invariant scattering convolution networks"

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.LG(Machine Learning)