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 deep learning framework for unsupervised affine and deformable image registration"
  Abstract: Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.

Connected through hyperedge HE-1 (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"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"

Connected through hyperedge HE-2 (induced by source paper "recursive cascaded networks for unsupervised medical image registration"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised 3d end to end medical image registration with volume tweening network"
    - "an unsupervised learning model for deformable medical image registration"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "accurate optical flow via direct cost volume processing"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised 3d end to end medical image registration with volume tweening network"):
  with members:
    - "recursive cascaded networks for unsupervised medical image registration"
    - "networks for joint affine and non parametric image registration"
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image 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"
    - "unsupervised end to end learning for deformable medical image registration"

Connected through hyperedge HE-4 (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"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "label driven weakly supervised learning for multimodal deformarle image registration"

Connected through hyperedge HE-5 (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"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep learning for bayesian brain mri 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"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "pulse sequence resilient fast brain segmentation"

Connected through hyperedge HE-7 (induced by source paper "one shot learning for deformable medical image registration and periodic motion tracking"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "learning a probabilistic model for diffeomorphic registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "birnet brain image registration using dual supervised fully convolutional networks"
    - "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-8 (induced by source paper "closing the gap between deep and conventional image registration using probabilistic dense displacement networks"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "estimation of large motion in lung ct by integrating regularized keypoint correspondences into dense deformable registration"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "flownet learning optical flow with convolutional networks"

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)