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: "fast predictive image registration"
  Abstract: We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D.

Connected through hyperedge HE-1 (induced by source paper "a survey on biomedical image captioning"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "networks for joint affine and non parametric image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airnet self supervised affine registration for 3d medical images using neural networks"
    - "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"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "region specific diffeomorphic metric mapping"):
  with members:
    - "networks for joint affine and non parametric image registration"
    - "metric learning for image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "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"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (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"
    - "label driven weakly supervised learning for multimodal deformarle 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"
    - "spatial transformer networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "automatic registration between cone beam ct and scanned surface via deep pose regression neural networks and clustered similarities"):
  with members:
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "a survey on deep learning in medical image analysis"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "a deep metric for multimodal registration"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "mutual information neural estimation in cnn based end to end medical image registration"):
  with members:
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-7 (induced by source paper "leveraging uncertainty estimates for predicting segmentation quality"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "detecting cancer metastases on gigapixel pathology images"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "a survey on deep learning in medical image analysis"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

Connected through hyperedge HE-8 (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"
    - "unsupervised end to end learning for deformable medical image registration"
    - "u net convolutional networks for biomedical image segmentation"
    - "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)