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: "an unsupervised learning model for deformable medical image registration"
  Abstract: We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a CNN, and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-2 (induced by source paper "a comparative study for non rigid image registration and rigid image registration"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-3 (induced by source paper "a spatiotemporal volumetric interpolation network for 4d dynamic medical image"):
  with members:
    - "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-4 (induced by source paper "deform gan an unsupervised learning model for deformable registration"):
  with members:
    - "unsupervised 3d end to end medical image registration with volume tweening network"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "adversarial deformation regularization for training image registration neural networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "kldivnet an unsupervised neural network for multi modality image registration"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "airlab autograd image registration laboratory"
    - "formal limitations on the measurement of mutual information"

Connected through hyperedge HE-6 (induced by source paper "a multiple decoder cnn for inverse consistent 3d image registration"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "coherent point drift networks unsupervised learning of non rigid point set registration"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "began boundary equilibrium generative adversarial networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "convolutional neural network architecture for geometric matching"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"
    - "3d shape reconstruction from sketches via multi view 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)