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 residual learning for accelerated mri using magnitude and phase networks"
  Abstract: Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. The proposed deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data is available, the proposed approach works as an image domain post-processing algorithm. Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. Comparisons using single and multiple coil show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing compressed sensing methods. The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.

Connected through hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "boosting cnn beyond label in inverse problems"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "k space deep learning for reference free epi ghost correction"
    - "understanding batch normalization"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "deep generative adversarial networks for compressed sensing automates mri"

Connected through hyperedge HE-2 (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"
    - "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"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "universal deep beamformer for variable rate ultrasound imaging"):
  with members:
    - "a deep learning framework for single sided sound speed inversion in medical ultrasound"
    - "sparse convolutional beamforming for ultrasound imaging"
    - "high quality ultrasonic multi line transmission through deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "high frame rate cardiac ultrasound imaging with deep learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning"

Connected through hyperedge HE-4 (induced by source paper "ifr net iterative feature refinement network for compressed sensing mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "deep convolutional neural network for inverse problems in imaging"
    - "convolutional recurrent neural networks for dynamic mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-5 (induced by source paper "deep generalization of structured low rank algorithms deep slr"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "structured low rank algorithms theory mr applications and links to machine learning"
    - "off the grid model based deep learning o modl"
    - "k space deep learning for accelerated mri"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "super resolution mri using finite rate of innovation curves"

Connected through hyperedge HE-6 (induced by source paper "self supervised deep active accelerated mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "unified theory for recovery of sparse signals in a general transform domain"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"

Connected through hyperedge HE-7 (induced by source paper "reconstructing multi echo magnetic resonance images via structured deep dictionary learning"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"

Connected through hyperedge HE-8 (induced by source paper "k space deep learning for accelerated mri"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "image reconstruction by domain transform manifold learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "compressive sampling using annihilating filter based low rank interpolation"

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)