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: "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
  Abstract: Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. 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 "medgan medical image translation using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "towards a deeper understanding of adversarial losses"
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "adversarial sparse view cbct artifact reduction"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-2 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "benefiting from multitask learning to improve single image super resolution"):
  with members:
    - "learn to synthesize and synthesize to learn"
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "learning to maintain natural image statistics"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "srpgan perceptual generative adversarial network for single image super resolution"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-4 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays"):
  with members:
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "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-5 (induced by source paper "variational adversarial active learning"):
  with members:
    - "uncertainty guided continual learning with bayesian neural networks"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cost sensitive active learning for intracranial hemorrhage detection"
    - "adversarial sampling for active learning"
    - "gradient free policy architecture search and adaptation"
    - "cost effective active learning for melanoma segmentation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-7 (induced by source paper "progressive generative adversarial networks for medical image super resolution"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "compression artifacts reduction by a deep convolutional network"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "an active learning approach for reducing annotation cost in skin lesion analysis"):
  with members:
    - "robust learning at noisy labeled medical images applied to skin lesion classification"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "mixup beyond empirical risk minimization"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"

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)