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: "frist flipping and rotation invariant sparsifying transform learning and applications"
  Abstract: Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. However, synthesis dictionary learning typically involves NP-hard sparse coding and expensive learning steps. Recently, sparsifying transform learning received interest for its cheap computation and its optimal updates in the alternating algorithms. In this work, we develop a methodology for learning Flipping and Rotation Invariant Sparsifying Transforms, dubbed FRIST, to better represent natural images that contain textures with various geometrical directions. The proposed alternating FRIST learning algorithm involves efficient optimal updates. We provide a convergence guarantee, and demonstrate the empirical convergence behavior of the proposed FRIST learning approach. Preliminary experiments show the promising performance of FRIST learning for sparse image representation, segmentation, denoising, robust inpainting, and compressed sensing-based magnetic resonance image reconstruction.

Connected through hyperedge HE-1 (induced by source paper "the power of complementary regularizers image recovery via transform learning and low rank modeling"):
  with members:
    - "non local recurrent network for image restoration"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "image denoising via group sparsity residual constraint"
    - "learning super resolution jointly from external and internal examples"

Connected through hyperedge HE-2 (induced by source paper "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning"):
  with members:
    - "image restoration by iterative denoising and backward projections"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "enhanced low rank matrix approximation"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "marlow a joint multiplanar autoregressive and low rank approach for image completion"
    - "robust pca via nonconvex rank approximation"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

Connected through hyperedge HE-3 (induced by source paper "group sparsity residual constraint for image denoising"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "non convex weighted lp minimization based group sparse representation framework for image denoising"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "adaptive image denoising by mixture adaptation"
    - "adaptive image denoising by targeted databases"
    - "boosting of image denoising algorithms"
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"

Connected through hyperedge HE-4 (induced by source paper "group based sparse representation for image compressive sensing reconstruction with non convex regularization"):
  with members:
    - "analyzing the group sparsity based on the rank minimization methods"
    - "image denoising via group sparsity residual constraint"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "structural group sparse representation for image compressive sensing recovery"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-5 (induced by source paper "vidosat high dimensional sparsifying transform learning for online video denoising"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "boosting of image denoising algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)