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: "label driven weakly supervised learning for multimodal deformarle image registration"
  Abstract: Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.

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"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "inference suboptimality in variational autoencoders"
    - "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 "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-3 (induced by source paper "metric learning for image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "fast predictive image registration"
    - "spatial transformer networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised learning of metric aggregations for deformable image registration"):
  with members:
    - "slice to volume medical image registration a survey"
    - "ssemnet serial section electron microscopy image registration using a spatial transformer network with learned features"
    - "deformable registration through learning of context specific metric aggregation"
    - "a deep metric for multimodal registration"
    - "spatial transformer networks"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "mlvirnet multilevel variational image registration network"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "an unsupervised learning model for deformable medical image registration"
    - "estimation of large motion in lung ct by integrating regularized keypoint correspondences into dense deformable registration"

Connected through hyperedge HE-6 (induced by source paper "mmfnet a multi modality mri fusion network for segmentation of nasopharyngeal carcinoma"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "yolov3 an incremental improvement"
    - "3d roi aware u net for accurate and efficient colorectal tumor segmentation"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cbam convolutional block attention module"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "brain tumor segmentation with deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "a deep learning framework for unsupervised affine and deformable image registration"):
  with members:
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "an artificial agent for robust image registration"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "spatial transformer networks"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised convolutional neural networks for multimodal image registration"):
  with members:
    - "quicksilver fast predictive image registration a deep learning approach"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "flownet learning optical flow with convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "representation learning a review and new perspectives"

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)