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: "bayesian nonparametric dictionary learning for compressed sensing mri"
  Abstract: We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled    \(k \)   -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing mri via a multi scale dilated residual convolution network"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "image super resolution using very deep residual channel attention networks"
    - "a multiscale image denoising algorithm based on dilated residual convolution network"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "depth reconstruction of translucent objects from a single time of flight camera using deep residual networks"
    - "lightweight pyramid networks for image deraining"
    - "a segmentation aware deep fusion network for compressed sensing mri"
    - "isointense infant brain mri segmentation with a dilated convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "a deep information sharing network for multi contrast compressed sensing mri reconstruction"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "opening the black box of deep neural networks via information"
    - "memnet a persistent memory network for image restoration"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "a segmentation aware deep fusion network for compressed sensing mri"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "deep contrast learning for salient object detection"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "a deep error correction network for compressed sensing mri"):
  with members:
    - "ifr net iterative feature refinement network for compressed sensing mri"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "image reconstruction by domain transform manifold learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "identity mappings in deep residual networks"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "coupled dictionary learning for multi contrast mri reconstruction"):
  with members:
    - "a deep information sharing network for multi contrast compressed sensing mri reconstruction"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "sparse modeling for image and vision processing"
    - "a compressed sensing framework for magnetic resonance fingerprinting"

Connected through hyperedge HE-6 (induced by source paper "a divide and conquer approach to compressed sensing mri"):
  with members:
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "forest sparsity for multi channel compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "lensless compressive imaging"):
  with members:
    - "compressive hyperspectral imaging with side information"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "from denoising to compressed sensing"
    - "a new compressive video sensing framework for mobile broadcast"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "efficient sum of outer products dictionary learning soup dil the ell_0 method"):
  with members:
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "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.CV(Computer Vision and Pattern Recognition)