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: "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
  Abstract: Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.

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 "attention driven tree structured convolutional lstm for high dimensional data understanding"):
  with members:
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "residual attention network for image classification"
    - "unsupervised learning for physical interaction through video prediction"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "deep sequential segmentation of organs in volumetric medical scans"):
  with members:
    - "densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation"
    - "lstm a search space odyssey"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "extracting 3d vascular structures from microscopy images using convolutional recurrent networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "3 d convolutional neural networks for glioblastoma segmentation"

Connected through hyperedge HE-4 (induced by source paper "spda superpixel based data augmentation for biomedical image segmentation"):
  with members:
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "low shot learning from imaginary data"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "mixup beyond empirical risk minimization"
    - "gland instance segmentation using deep multichannel neural networks"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "gland instance segmentation by deep multichannel side supervision"

Connected through hyperedge HE-5 (induced by source paper "uncertainty aware self ensembling model for semi supervised 3d left atrium segmentation"):
  with members:
    - "semi supervised brain lesion segmentation with an adapted mean teacher model"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "deep semi supervised segmentation with weight averaged consistency targets"
    - "multi task learning for left atrial segmentation on ge mri"
    - "semi supervised deep learning for fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "cascade decoder a universal decoding method for biomedical image segmentation"):
  with members:
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "a new registration approach for dynamic analysis of calcium signals in organs"
    - "neuron segmentation using deep complete bipartite networks"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (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"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "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-8 (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"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

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)