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: "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
  Abstract: We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations. We model registration in a probabilistic and generative fashion, by applying a conditional variational autoencoder (CVAE) network. This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space. Most recent learning-based registration algorithms use supervised labels or deformation models, that miss important properties such as diffeomorphism and sufficiently regular deformation fields. In this work, we constrain transformations to be diffeomorphic by using a differentiable exponentiation layer with a symmetric loss function. We evaluated our method on 330 cardiac MR sequences and demonstrate robust intra-subject registration results comparable to two state-of-the-art methods but with more regular deformation fields compared to a recent learning-based algorithm. Our method reached a mean DICE score of 78.3% and a mean Hausdorff distance of 7.9mm. In two preliminary experiments, we illustrate the model's abilities to transport pathological deformations to healthy subjects and to cluster five diseases in the unsupervised deformation encoding space with a classification performance of 70%.

Connected through hyperedge HE-1 (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"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "an unsupervised learning model for deformable medical image 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-2 (induced by source paper "learning a probabilistic model for diffeomorphic registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "generative adversarial networks for mr ct deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "birnet brain image registration using dual supervised fully convolutional networks"
    - "learning myelin content in multiple sclerosis from multimodal mri through adversarial training"
    - "an unsupervised learning model for deformable medical image registration"
    - "learning interpretable anatomical features through deep generative models application to cardiac remodeling"
    - "dual motion gan for future flow embedded video prediction"

Connected through hyperedge HE-3 (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"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image 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-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"
    - "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"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-5 (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"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image 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"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deformable registration for multi modal images via disentangled representations"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "multimodal unsupervised image to image translation"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "improved training of wasserstein gans"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (induced by source paper "probabilistic multilayer regularization network for unsupervised 3d brain image registration"):
  with members:
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "spatial transformer networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "learning based regularization for cardiac strain analysis with ability for domain adaptation"

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)