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: "non rigid image registration using fully convolutional networks with deep self supervision"
  Abstract: We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. At the same time, our method also learns FCNs for encoding the spatial transformations at the same spatial resolution of images to be registered, rather than learning coarse-grained spatial transformation information. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different resolutions with deep self-supervision through typical feedforward and backpropagation computation. Since our method simultaneously optimizes and learns spatial transformations for the image registration, our method can be directly used to register a pair of images, and the registration of a set of images is also a training procedure for FCNs so that the trained FCNs can be directly adopted to register new images by feedforward computation of the learned FCNs without any optimization. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.

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"
    - "an unsupervised learning model for deformable medical image registration"
    - "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"
    - "an unsupervised learning model for deformable medical image registration"
    - "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 "voxelmorph a learning framework for deformable medical image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "iterative amortized inference"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "inference suboptimality in variational autoencoders"
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "advances in variational inference"

Connected through hyperedge HE-5 (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"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "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-6 (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-7 (induced by source paper "on reducing negative jacobian determinant of the deformation predicted by deep registration networks"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "inverse consistent deep networks for unsupervised deformable image registration"
    - "ssemnet serial section electron microscopy image registration using a spatial transformer network with learned features"
    - "unsupervised end to end learning for deformable medical image registration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-8 (induced by source paper "developing brain atlas through deep learning"):
  with members:
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mask r cnn"
    - "a large deformation diffeomorphic approach to registration of clarity images via mutual information"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "feature pyramid networks for object detection"
    - "sub cortical brain structure segmentation using f cnn s"
    - "deep residual learning for image recognition"
    - "fast r cnn"

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)