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 learning of probabilistic diffeomorphic registration for images and surfaces"
  Abstract: Abstract   Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates.  In this paper, we build a connection between classical and learning-based methods. We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task for both images and anatomical surfaces, and provide extensive empirical analyses of the algorithm. Our principled approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees. Our implementation is available online at  http://voxelmorph.csail.mit.edu .

Connected through hyperedge HE-1 (induced by source paper "volumetric landmark detection with a multi scale shift equivariant neural network"):
  with members:
    - "making convolutional networks shift invariant again"
    - "cnn based landmark detection in cardiac cta scans"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning conditional deformable templates with convolutional networks"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "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"
    - "quicksilver fast predictive image registration a deep learning approach"

Connected through hyperedge HE-3 (induced by source paper "few labeled atlases are necessary for deep learning based segmentation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "learning a probabilistic model for diffeomorphic registration"
    - "semi supervised and task driven data augmentation"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-4 (induced by source paper "atlas based segmentations via semi supervised diffeomorphic registrations"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised 3d end to end medical image registration with volume tweening network"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "infovae information maximizing variational autoencoders"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"

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)