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: "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
  Abstract: We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD). #R##N#Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive field of 131x131 voxels was trained for myocardium and blood pool segmentation in axial, sagittal and coronal image slices. Performance was evaluated within the HVSMR challenge. #R##N#Automatic segmentation of the test scans resulted in Dice indices of 0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of 0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76 and 7.07$\pm$3.01 mm for the myocardium and blood pool, respectively. Segmentation took 41.5$\pm$14.7 s per scan. #R##N#In conclusion, dilated CNNs trained on a small set of CMR images of CHD patients showing large anatomical variability provide accurate myocardium and blood pool segmentations.

Connected through hyperedge HE-1 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

Connected through hyperedge HE-2 (induced by source paper "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "neural style transfer improves 3d cardiovascular mr image segmentation on inconsistent data"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "a new ensemble learning framework for 3d biomedical image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "coronary artery centerline extraction in cardiac ct angiography using a cnn based orientation classifier"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "neural machine translation in linear time"
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "unbiased shape compactness for segmentation"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "gridnet with automatic shape prior registration for automatic mri cardiac segmentation"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-6 (induced by source paper "a new ensemble learning framework for 3d biomedical image segmentation"):
  with members:
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "ensembling neural networks for digital pathology images classification and segmentation"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "xgboost a scalable tree boosting system"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "ternarynet faster deep model inference without gpus for medical 3d segmentation using sparse and binary convolutions"):
  with members:
    - "faster population counts using avx2 instructions"
    - "hashnet deep learning to hash by continuation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "densely connected convolutional networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "automatic segmentation of thoracic aorta segments in low dose chest ct"):
  with members:
    - "convnet based localization of anatomical structures in 3 d medical images"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)