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 self supervised fully convolutional networks without training data"
  Abstract: A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. 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. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different spatial resolutions with deep self-supervision through typical feedforward and backpropagation computation. 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 "a comparative study for non rigid image registration and rigid image registration"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "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-2 (induced by source paper "multimodal self supervised learning for medical image analysis"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "domain generalization by solving jigsaw puzzles"
    - "deep learning versus classical regression for brain tumor patient survival prediction"
    - "no new net"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "deep morphological simplification network ms net for guided registration of brain magnetic resonance images"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "adversarial deformation regularization for training image registration neural networks"
    - "an unsupervised learning model for deformable medical image registration"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "an artificial agent for robust image registration"

Connected through hyperedge HE-5 (induced by source paper "conv2warp an unsupervised deformable image registration with continuous convolution and warping"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "deformable convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "faim a convnet method for unsupervised 3d medical image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "inverse consistent deep networks for unsupervised deformable image registration"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "unsupervised end to end learning for deformable medical image registration"
    - "fast predictive image registration"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning for fast probabilistic diffeomorphic registration"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "localization a missing link in the pipeline of object matching and registration"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "convolutional neural network architecture for geometric matching"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "ssd single shot multibox detector"
    - "fast predictive image registration"
    - "spatial transformer networks"
    - "deepmatching hierarchical deformable dense matching"

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)