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: "informative sample generation using class aware generative adversarial networks for classification of chest xrays"
  Abstract: Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods.

Connected through hyperedge HE-1 (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"
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "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-2 (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"
    - "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"
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "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-3 (induced by source paper "gan based medical image registration"):
  with members:
    - "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"
    - "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)