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: "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
  Abstract: The sparsity of signals in a transform domain or dictionary has been exploited in applications, such as compression, denoising, and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically nonconvex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speed-ups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction.

Connected through hyperedge HE-1 (induced by source paper "analysis of fast structured dictionary learning"):
  with members:
    - "learning multi layer transform models"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "a fast patch dictionary method for whole image recovery"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-2 (induced by source paper "cnn based projected gradient descent for consistent ct image reconstruction"):
  with members:
    - "deep learning with domain adaptation for accelerated projection reconstruction mr"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "deep learning for photoacoustic tomography from sparse data"
    - "recurrent inference machines for solving inverse problems"
    - "skip connections as effective symmetry breaking"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-3 (induced by source paper "analysis of fast alternating minimization for structured dictionary learning"):
  with members:
    - "analysis of fast structured dictionary learning"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "a fast patch dictionary method for whole image recovery"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-4 (induced by source paper "convolutional dictionary learning acceleration and convergence"):
  with members:
    - "convolutional sparse coding for high dynamic range imaging"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "optimization methods for convolutional sparse coding"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"
    - "fast stochastic alternating direction method of multipliers"

Connected through hyperedge HE-5 (induced by source paper "learning multi layer transform models"):
  with members:
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "analysis of fast structured dictionary learning"
    - "vidosat high dimensional sparsifying transform learning for online video denoising"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "robust photometric stereo via dictionary learning"):
  with members:
    - "single image rgb photometric stereo with spatially varying albedo"
    - "from shading to local shape"
    - "efficient sum of outer products dictionary learning soup dil the ell_0 method"
    - "a dictionary based approach for estimating shape and spatially varying reflectance"
    - "efficient sum of sparse outer products dictionary learning soup dil"

Connected through hyperedge HE-7 (induced by source paper "efficient sum of sparse outer products dictionary learning soup dil"):
  with members:
    - "a fast patch dictionary method for whole image recovery"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "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.LG(Machine Learning)