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 compressed sensing ct reconstruction via persistent homology analysis"
  Abstract: Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive. To address this issue, here we propose a novel deep residual learning approach for sparse view CT reconstruction. Specifically, based on a novel persistent homology analysis showing that the manifold of streaking artifacts is topologically simpler than original ones, a deep residual learning architecture that estimates the streaking artifacts is developed. Once a streaking artifact image is estimated, an artifact-free image can be obtained by subtracting the streaking artifacts from the input image. Using extensive experiments with real patient data set, we confirm that the proposed residual learning provides significantly better image reconstruction performance with several orders of magnitude faster computational speed.

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"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "understanding batch normalization"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"

Connected through hyperedge HE-2 (induced by source paper "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"):
  with members:
    - "model based learning for accelerated limited view 3d photoacoustic tomography"
    - "deep learning for photoacoustic tomography from sparse data"
    - "deep convolutional neural network for inverse problems in imaging"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "accurate image super resolution using very deep convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a topological loss function for deep learning based image segmentation using persistent homology"):
  with members:
    - "phom gem persistent homology for generative models"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "a topology layer for machine learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "connectivity optimized representation learning via persistent homology"
    - "toporeg a topological regularizer for classifiers"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"

Connected through hyperedge HE-4 (induced by source paper "improving generalization of deep networks for inverse reconstruction of image sequences"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "deep variational information bottleneck"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "context encoders feature learning by inpainting"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep residual learning for accelerated mri using magnitude and phase networks"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "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"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "random mesh projectors for inverse problems"):
  with members:
    - "adversarial regularizers in inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "deep image prior"
    - "deep learning for photoacoustic tomography from sparse data"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep learning guided image reconstruction from incomplete data"
    - "maximal sparsity with deep networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "deepnis deep neural network for nonlinear electromagnetic inverse scattering"):
  with members:
    - "lensless computational imaging through deep learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"
    - "depth map prediction from a single image using a multi scale deep network"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "one network to solve all rois deep learning ct for any roi using differentiated backprojection"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "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"
    - "deep convolutional neural network for inverse problems in imaging"
    - "u net convolutional networks for biomedical image segmentation"
    - "matconvnet convolutional neural networks for matlab"

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)