human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gan based medical image registration"):
  Abstract: Conventional approaches to image registration consist of time consuming iterative methods. Most current deep learning (DL) based registration methods extract deep features to use in an iterative setting. We propose an end-to-end DL method for registering multimodal images. Our approach uses generative adversarial networks (GANs) that eliminates the need for time consuming iterative methods, and directly generates the registered image with the deformation field. Appropriate constraints in the GAN cost function produce accurately registered images in less than a second. Experiments demonstrate their accuracy for multimodal retinal and cardiac MR image registration.

Includes members:
  Member "informative sample generation using class aware generative adversarial networks for classification of chest xrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "amd severity prediction and explainability using image registration and deep embedded clustering")
  Member "training data independent image registration with gans using transfer learning and segmentation information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays")
  Member "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty guided domain alignment for layer segmentation in oct images")
  Member "end to end unsupervised deformable image registration with a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "an artificial agent for robust image registration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning in healthcare a survey")

Question: Please tell me which class the source paper 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)