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 end to end learning for deformable medical image registration"
  Abstract: We propose a registration algorithm for 2D CT/MRI medical images with a new unsupervised end-to-end strategy using convolutional neural networks. The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems. The image-to-image integrated framework can simultaneously learn both image features and transformation matrix for registration. (2) Training with additional data without any label can further improve the registration performance by approximately 10 %. (3) The registration speed is 100x faster than traditional methods. The proposed network is easy to implement and can be trained efficiently. Experiments demonstrate that our system achieves state-of-the-art results on 2D brain registration and achieves comparable results on 2D liver registration. It can be extended to register other organs beyond liver and brain such as kidney, lung, and heart.

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 "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"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"

Connected through hyperedge HE-3 (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"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "ssemnet serial section electron microscopy image registration using a spatial transformer network with learned features"
    - "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-4 (induced by source paper "applying transfer learning to deep learned models for eeg analysis"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "deep learning with convolutional neural networks for decoding and visualization of eeg pathology"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deep learning in neural networks"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"
    - "transfer learning for video recognition with scarce training data for deep convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "faim a convnet method for unsupervised 3d medical image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "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"
    - "fast predictive image registration"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-6 (induced by source paper "airnet self supervised affine registration for 3d medical images using neural networks"):
  with members:
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "densely connected convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "spatial transformer networks"
    - "real time 2d 3d registration via cnn regression"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "inverse consistent deep networks for unsupervised deformable image registration"):
  with members:
    - "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"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"

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)