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: "weakly supervised convolutional neural networks for multimodal image registration"
  Abstract: Abstract   One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific  ad hoc  landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while  only  unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.

Connected through hyperedge HE-1 (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"
    - "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"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "advances in variational inference"

Connected through hyperedge HE-2 (induced by source paper "learning a probabilistic model for diffeomorphic registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "generative adversarial networks for mr ct deformable image registration"
    - "birnet brain image registration using dual supervised fully convolutional networks"
    - "learning myelin content in multiple sclerosis from multimodal mri through adversarial training"
    - "an unsupervised learning model for deformable medical image registration"
    - "learning interpretable anatomical features through deep generative models application to cardiac remodeling"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "dual motion gan for future flow embedded video prediction"

Connected through hyperedge HE-3 (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"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "label driven weakly supervised learning for multimodal deformarle image registration"

Connected through hyperedge HE-4 (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"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-5 (induced by source paper "recurrent registration neural networks for deformable image registration"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airlab autograd image registration laboratory"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "dilated fcn for multi agent 2d 3d medical image registration"
    - "an artificial agent for robust image registration"
    - "spatial transformer networks"

Connected through hyperedge HE-6 (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"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "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-7 (induced by source paper "closing the gap between deep and conventional image registration using probabilistic dense displacement networks"):
  with members:
    - "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"
    - "estimation of large motion in lung ct by integrating regularized keypoint correspondences into dense deformable registration"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "flownet learning optical flow with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised deformable registration for multi modal images via disentangled representations"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "multimodal unsupervised image to image translation"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "an unsupervised learning model for deformable medical image registration"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "improved training of wasserstein gans"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "spatial transformer 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)