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: "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
  Abstract: Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our approach is based on a multi-view convolutional neural network (CNN) with an adaptive fusion strategy and a new loss function that allows fast and more accurate convergence of the backpropagation based optimization. After training our network from scratch by using more than 60K 2D MRI images (slices), we have evaluated our segmentation strategy to the STACOM 2013 cardiac segmentation challenge benchmark. Qualitative and quantitative evaluations, obtained from the segmentation challenge, indicate that the proposed method achieved the state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).

Connected through hyperedge HE-1 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a survey on deep transfer learning"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "deep contextualized word representations"
    - "video salient object detection via fully convolutional networks"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"):
  with members:
    - "the deep poincar e map a novel approach for left ventricle segmentation"
    - "dilated residual networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"
    - "deeply recursive convolutional network for image super resolution"
    - "image super resolution using deep convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "instance level microtubule segmentation using recurrent attention"):
  with members:
    - "maskrnn instance level video object segmentation"
    - "deep geodesic learning for segmentation and anatomical landmarking"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "end to end instance segmentation with recurrent attention"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "automatic 3d bi ventricular segmentation of cardiac images by a shape constrained multi task deep learning approach"):
  with members:
    - "learning based quality control for cardiac mr images"
    - "deep nested level sets fully automated segmentation of cardiac mr images in patients with pulmonary hypertension"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "left ventricle segmentation in cardiac mr images using fully convolutional network"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep geodesic learning for segmentation and anatomical landmarking"):
  with members:
    - "limits of end to end learning"
    - "failures of gradient based deep learning"
    - "searching for activation functions"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "a survey of geodesic paths on 3d surfaces"

Connected through hyperedge HE-6 (induced by source paper "pyramid network with online hard example mining for accurate left atrium segmentation"):
  with members:
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "training region based object detectors with online hard example mining"
    - "pyramid scene parsing network"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "dilated convolutions in neural networks for left atrial segmentation in 3d gadolinium enhanced mri"):
  with members:
    - "combating uncertainty with novel losses for automatic left atrium segmentation"
    - "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
    - "pyramid network with online hard example mining for accurate left atrium segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-8 (induced by source paper "multiview two task recursive attention model for left atrium and atrial scars segmentation"):
  with members:
    - "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
    - "residual attention network for image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)