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: "joint learning of motion estimation and segmentation for cardiac mr image sequences"
  Abstract: Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed.

Connected through hyperedge HE-1 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "learning a probabilistic model for diffeomorphic registration"
    - "deep compressed sensing"
    - "direct object recognition without line of sight using optical coherence"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "selflow self supervised learning of optical flow"
    - "learning to self train for semi supervised few shot classification"
    - "unsupervised event based learning of optical flow depth and egomotion"

Connected through hyperedge HE-2 (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-3 (induced by source paper "temporal consistency objectives regularize the learning of disentangled representations"):
  with members:
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "recurrent neural networks for aortic image sequence segmentation with sparse annotations"
    - "learning to decompose and disentangle representations for video prediction"
    - "on the effectiveness of least squares generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "improved training of wasserstein gans"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "probabilistic motion modeling from medical image sequences application to cardiac cine mri"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-6 (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"
    - "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"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"

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)