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: "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
  Abstract: We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images that we use to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior enables fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code, model definitions and model weights are freely available at http://github.com/adalca/neuron.

Connected through hyperedge HE-1 (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"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-2 (induced by source paper "towards learning a self inverse network for bidirectional image to image translation"):
  with members:
    - "towards instance level image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "analyzing inverse problems with invertible neural networks"
    - "multimodal unsupervised image to image translation"
    - "i revnet deep invertible networks"
    - "drit diverse image to image translation via disentangled representations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-3 (induced by source paper "task decomposition and synchronization for semantic biomedical image segmentation"):
  with members:
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "a survey on multi task learning"

Connected through hyperedge HE-4 (induced by source paper "data augmentation using learned transformations for one shot medical image segmentation"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "nonparametric density flows for mri intensity normalisation"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "few shot segmentation propagation with guided networks"
    - "autoaugment learning augmentation policies from data"
    - "an unsupervised learning model for deformable medical image registration"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised data imputation via variational inference of deep subspaces"):
  with members:
    - "medical image imputation from image collections"
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "submanifold sparse convolutional networks"
    - "sparsity invariant cnns"
    - "learning structured sparsity in deep neural networks"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-6 (induced by source paper "learning conditional deformable templates with convolutional networks"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "quicksilver fast predictive image registration a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "metric learning for image registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "label driven weakly supervised learning for multimodal deformarle image registration"

Connected through hyperedge HE-8 (induced by source paper "few labeled atlases are necessary for deep learning based segmentation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "learning a probabilistic model for diffeomorphic registration"
    - "semi supervised and task driven data augmentation"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "cnn based segmentation of medical imaging data"

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)