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: "a tensor based dictionary learning approach to tomographic image reconstruction"
  Abstract: We consider tomographic reconstruction using priors in the form of a dictionary learned from training images. The reconstruction has two stages: first we construct a tensor dictionary prior from our training data, and then we pose the reconstruction problem in terms of recovering the expansion coefficients in that dictionary. Our approach differs from past approaches in that a) we use a third-order tensor representation for our images and b) we recast the reconstruction problem using the tensor formulation. The dictionary learning problem is presented as a non-negative tensor factorization problem with sparsity constraints. The reconstruction problem is formulated in a convex optimization framework by looking for a solution with a sparse representation in the tensor dictionary. Numerical results show that our tensor formulation leads to very sparse representations of both the training images and the reconstructions due to the ability of representing repeated features compactly in the dictionary.

Connected through hyperedge HE-1 (induced by source paper "non negative tensor patch dictionary approaches for image compression and deblurring applications"):
  with members:
    - "tomographic image reconstruction using training images"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"

Connected through hyperedge HE-2 (induced by source paper "stable tensor neural networks for rapid deep learning"):
  with members:
    - "stable architectures for deep neural networks"
    - "learning across scales a multiscale method for convolution neural networks"
    - "failures of gradient based deep learning"
    - "deep vs shallow networks an approximation theory perspective"
    - "tensorizing neural networks"
    - "deep residual learning for image recognition"
    - "the limitations of deep learning in adversarial settings"
    - "predicting parameters in deep learning"

Connected through hyperedge HE-3 (induced by source paper "minimax lower bounds on dictionary learning for tensor data"):
  with members:
    - "minimax lower bounds for kronecker structured dictionary learning"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"

Connected through hyperedge HE-4 (induced by source paper "vectorial dimension reduction for tensors based on bayesian inference"):
  with members:
    - "tensorizing neural networks"
    - "dictionary learning and sparse coding for third order super symmetric tensors"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "a bayesian tensor factorization model via variational inference for link prediction"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"

Connected through hyperedge HE-5 (induced by source paper "minimax lower bounds for kronecker structured dictionary learning"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-6 (induced by source paper "tomographic image reconstruction using training images"):
  with members:
    - "bayesian nonparametric dictionary learning for compressed sensing mri"
    - "dictionary learning based reconstruction method for electron tomography"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "denoising and completion of 3d data via multidimensional dictionary learning"):
  with members:
    - "convolutional dictionary learning through tensor factorization"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

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)