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: "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
  Abstract: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.

Connected through hyperedge HE-1 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "transparency motivations and challenges"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"

Connected through hyperedge HE-2 (induced by source paper "disentangled representation learning in cardiac image analysis"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "multimodal unsupervised image to image translation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a variational u net for conditional appearance and shape generation"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "scalable multimodal convolutional networks for brain tumour segmentation"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning for cardiac mr image segmentation by anatomical position prediction"):
  with members:
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "unsupervised representation learning by predicting image rotations"
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
    - "multi task self supervised visual learning"
    - "self supervised learning for spinal mris"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-4 (induced by source paper "deep learning cardiac motion analysis for human survival prediction"):
  with members:
    - "automatic 3d bi ventricular segmentation of cardiac images by a shape constrained multi task deep learning approach"
    - "learning interpretable anatomical features through deep generative models application to cardiac remodeling"
    - "deep representation learning for human motion prediction and classification"
    - "a survey on deep learning in medical image analysis"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "discriminative recurrent sparse auto encoders"

Connected through hyperedge HE-5 (induced by source paper "assessing the impact of blood pressure on cardiac function using interpretable biomarkers and variational autoencoders"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "learning interpretable anatomical features through deep generative models application to cardiac remodeling"

Connected through hyperedge HE-6 (induced by source paper "learning based quality control for cardiac mr images"):
  with members:
    - "fast edge detection using structured forests"

Connected through hyperedge HE-7 (induced by source paper "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "fastventricle cardiac segmentation with enet"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic segmentation and disease classification using cardiac cine mr images"
    - "2d 3d fully convolutional neural networks for cardiac mr segmentation"

Connected through hyperedge HE-8 (induced by source paper "attention u net learning where to look for the pancreas"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "ternarynet faster deep model inference without gpus for medical 3d segmentation using sparse and binary convolutions"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "learn to pay attention"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "non local neural networks"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "squeeze and excitation networks"

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)