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: "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"
  Abstract: Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus those of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other methods, respectively.

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 "a distance map regularized cnn for cardiac cine mr image segmentation"):
  with members:
    - "deep visual domain adaptation a survey"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "left ventricle segmentation and quantification from cardiac cine mr images via multi task learning"
    - "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"
    - "a survey on deep learning in medical image analysis"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "fully hyperbolic convolutional neural networks"):
  with members:
    - "fast video object segmentation with spatio temporal gans"
    - "scale recurrent network for deep image deblurring"
    - "deep neural networks motivated by partial differential equations"
    - "switchable temporal propagation network"
    - "multi level wavelet cnn for image restoration"
    - "the reversible residual network backpropagation without storing activations"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"

Connected through hyperedge HE-4 (induced by source paper "imexnet a forward stable deep neural network"):
  with members:
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "neural ordinary differential equations"
    - "nais net stable deep networks from non autonomous differential equations"
    - "the reversible residual network backpropagation without storing activations"
    - "visualizing the loss landscape of neural nets"
    - "reversible architectures for arbitrarily deep residual neural networks"

Connected through hyperedge HE-5 (induced by source paper "exploiting computation power of blockchain for biomedical image segmentation"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "blockchain technology overview"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "quantization of fully convolutional networks for accurate biomedical image segmentation"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-6 (induced by source paper "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"):
  with members:
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "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"
    - "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-7 (induced by source paper "estimation of the volume of the left ventricle from mri images using deep neural networks"):
  with members:
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deconvolution network for semantic segmentation"
    - "hypercolumns for object segmentation and fine grained localization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "automatic quantification of the lv function and mass a deep learning approach for cardiovascular mri"):
  with members:
    - "automatic myocardial segmentation by using a deep learning network in cardiac mri"
    - "a survey on deep learning in medical image analysis"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)