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: "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
  Abstract: Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy ( e.g.  shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks ( e.g.  image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.

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"
    - "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"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"

Connected through hyperedge HE-2 (induced by source paper "pose aware instance segmentation framework from cone beam ct images for tooth segmentation"):
  with members:
    - "automatic registration between cone beam ct and scanned surface via deep pose regression neural networks and clustered similarities"
    - "yolov3 an incremental improvement"
    - "brain tumor segmentation with deep neural networks"
    - "random erasing data augmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "improved regularization of convolutional neural networks with cutout"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "liver segmentation in abdominal ct images via auto context neural network and self supervised contour attention"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "deeply self supervising edge to contour neural network applied to liver segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "brain tumor segmentation with deep neural networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-4 (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"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"

Connected through hyperedge HE-5 (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-6 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-7 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "el gan embedding loss driven generative adversarial networks for lane detection"

Connected through hyperedge HE-8 (induced by source paper "a distance map regularized cnn for cardiac cine mr image segmentation"):
  with members:
    - "deep visual domain adaptation a survey"
    - "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"
    - "a review on deep learning techniques applied to 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)