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: "fastventricle cardiac segmentation with enet"
  Abstract: Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-the-art in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4x faster and runs with 6x less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.

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 "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"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "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-3 (induced by source paper "segmentation of the proximal femur from mr images using deep convolutional neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cnn based segmentation of medical imaging data"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"):
  with members:
    - "melanogans high resolution skin lesion synthesis with gans"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "deep mr to ct synthesis using unpaired data"
    - "rendergan generating realistic labeled data"

Connected through hyperedge HE-5 (induced by source paper "multi task learning for left atrial segmentation on ge mri"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-6 (induced by source paper "a generative adversarial model for right ventricle segmentation"):
  with members:
    - "blitznet a real time deep network for scene understanding"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "context encoders feature learning by inpainting"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"

Connected through hyperedge HE-7 (induced by source paper "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "omega net fully automatic multi view cardiac mr detection orientation and segmentation with deep neural networks"):
  with members:
    - "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "learning deconvolution network for semantic segmentation"

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)