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: "real time 2d 3d registration via cnn regression"
  Abstract: In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the Digitally Reconstructed Radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. The CNN regressors are trained for local zones and applied in a hierarchical manner to break down the complex regression task into simpler sub-tasks that can be learned separately. Our experiment results demonstrate the advantage of the proposed method in computational efficiency with negligible degradation of registration accuracy compared to intensity-based methods.

Connected through hyperedge HE-1 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"

Connected through hyperedge HE-2 (induced by source paper "real time deep registration with geodesic loss"):
  with members:
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "3d pose regression using convolutional neural networks"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "3d reconstruction in canonical co ordinate space from arbitrarily oriented 2d images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-3 (induced by source paper "gan based medical image registration"):
  with members:
    - "informative sample generation using class aware generative adversarial networks for classification of chest xrays"
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "an artificial agent for robust image registration"

Connected through hyperedge HE-4 (induced by source paper "3d regression neural network for the quantification of enlarged perivascular spaces in brain mri"):
  with members:
    - "gp unet lesion detection from weak labels with a 3d regression network"
    - "segmentation of intracranial arterial calcification with deeply supervised residual dropout networks"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "end to end instance segmentation with recurrent attention"
    - "learning to count with deep object features"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

Connected through hyperedge HE-5 (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"
    - "unsupervised end to end learning for deformable medical image registration"
    - "densely connected convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "end to end unsupervised deformable image registration with a convolutional neural network"):
  with members:
    - "an artificial agent for robust image registration"
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "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)