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: "deep artifact learning for compressed sensing and parallel mri"
  Abstract: Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is one of the powerful ways to reduce the scan time of MR imaging with performance guarantee. However, the computational costs are usually expensive. This paper aims to propose a computationally fast and accurate deep learning algorithm for the reconstruction of MR images from highly down-sampled k-space data. #R##N#Theory: Based on the topological analysis, we show that the data manifold of the aliasing artifact is easier to learn from a uniform subsampling pattern with additional low-frequency k-space data. Thus, we develop deep aliasing artifact learning networks for the magnitude and phase images to estimate and remove the aliasing artifacts from highly accelerated MR acquisition. #R##N#Methods: The aliasing artifacts are directly estimated from the distorted magnitude and phase images reconstructed from subsampled k-space data so that we can get an aliasing-free images by subtracting the estimated aliasing artifact from corrupted inputs. Moreover, to deal with the globally distributed aliasing artifact, we develop a multi-scale deep neural network with a large receptive field. #R##N#Results: The experimental results confirm that the proposed deep artifact learning network effectively estimates and removes the aliasing artifacts. Compared to existing CS methods from single and multi-coli data, the proposed network shows minimal errors by removing the coherent aliasing artifacts. Furthermore, the computational time is by order of magnitude faster. #R##N#Conclusion: As the proposed deep artifact learning network immediately generates accurate reconstruction, it has great potential for clinical applications.

Connected through hyperedge HE-1 (induced by source paper "motion artifact reduction using a convolutional neural network for dynamic contrast enhanced mr imaging of the liver"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep de aliasing for fast compressive sensing mri"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep de aliasing for fast compressive sensing mri"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "highly scalable image reconstruction using deep neural networks with bandpass filtering"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "deep complex networks"
    - "convolutional neural networks for non iterative reconstruction of compressively sensed images"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "convolutional recurrent neural networks for dynamic mr image reconstruction"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "on the global local dichotomy in sparsity modeling"
    - "deep learning with domain adaptation for accelerated projection reconstruction mr"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "learning deep cnn denoiser prior for image restoration"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-5 (induced by source paper "deep de aliasing for fast compressive sensing mri"):
  with members:
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "conditional generative adversarial nets"

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)