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: "training data independent image registration with gans using transfer learning and segmentation information"
  Abstract: Registration is an important task in automated medical image analysis. Although deep learning (DL) based image registration methods out perform time consuming conventional approaches, they are heavily dependent on training data and do not generalize well for new images types. We present a DL based approach that can register an image pair which is different from the training images. This is achieved by training generative adversarial networks (GANs) in combination with segmentation information and transfer learning. Experiments on chest Xray and brain MR images show that our method gives better registration performance over conventional methods.

Connected through hyperedge HE-1 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "cost effective active learning for deep image classification"
    - "gans for biological image synthesis"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end unsupervised deformable image registration with a convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "amd severity prediction and explainability using image registration and deep embedded clustering"):
  with members:
    - "regression concept vectors for bidirectional explanations in histopathology"
    - "informative sample generation using class aware generative adversarial networks for classification of chest xrays"
    - "automatic brain tumor grading from mri data using convolutional neural networks and quality assessment"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "an unsupervised learning model for deformable medical image registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"):
  with members:
    - "night time haze and glow removal using deep dilated convolutional network"
    - "informative sample generation using class aware generative adversarial networks for classification of chest xrays"
    - "progressive generative adversarial networks for medical image super resolution"
    - "combining transfer learning and segmentation information with gans for training data independent image registration"
    - "amd severity prediction and explainability using image registration and deep embedded clustering"
    - "szoom a framework for automatic zoom into high resolution surveillance videos"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"
    - "gan based medical image registration"

Connected through hyperedge HE-4 (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"
    - "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"
    - "photo realistic single image super resolution using a generative adversarial network"

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)