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: "albumentations fast and flexible image augmentations"
  Abstract: Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels. In computer vision, image augmentations have become a common implicit regularization technique to combat overfitting in deep learning models and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations of flipping, rotating, scaling, and cropping. Moreover, image processing speed varies in existing image augmentation libraries. We present Albumentations, a fast and flexible open source library for image augmentation with many various image transform operations available that is also an easy-to-use wrapper around other augmentation libraries. We discuss the design principles that drove the implementation of Albumentations and give an overview of the key features and distinct capabilities. Finally, we provide examples of image augmentations for different computer vision tasks and demonstrate that Albumentations is faster than other commonly used image augmentation tools on most image transform operations.

Connected through hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "how much position information do convolutional neural networks encode"
    - "disentangling monocular 3d object detection"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"
    - "canonical surface mapping via geometric cycle consistency"

Connected through hyperedge HE-2 (induced by source paper "dense dilated convolutions merging network for land cover classification"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "on the convergence of adam and beyond"
    - "dense dilated convolutions merging network for semantic mapping of remote sensing images"
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "uncertainty gated network for land cover segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"

Connected through hyperedge HE-3 (induced by source paper "deep learning approach to diabetic retinopathy detection"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "transfer learning based detection of diabetic retinopathy from small dataset"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "on first order meta learning algorithms"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"
    - "grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "deep convolutions for in depth automated rock typing"):
  with members:
    - "adapting convolutional neural networks for geographical domain shift"
    - "augmentor an image augmentation library for machine learning"
    - "improved regularization of convolutional neural networks with cutout"
    - "understanding deep learning requires rethinking generalization"
    - "why should i trust you explaining the predictions of any classifier"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "deblurgan v2 deblurring orders of magnitude faster and better"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "gated fusion network for joint image deblurring and super resolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"

Connected through hyperedge HE-6 (induced by source paper "adapting convolutional neural networks for geographical domain shift"):
  with members:
    - "are all training examples created equal an empirical study"
    - "deep visual domain adaptation a survey"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "cycada cycle consistent adversarial domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-7 (induced by source paper "safe augmentation learning task specific transformations from data"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "autoaugment learning augmentation policies from data"
    - "data augmentation instead of explicit regularization"

Connected through hyperedge HE-8 (induced by source paper "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "sfa gtm seismic facies analysis based on generative topographic map and rbf"
    - "automatic salt deposits segmentation a deep learning approach"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep unsupervised learning of visual similarities"
    - "unsupervised representation learning by sorting sequences"
    - "deep unsupervised similarity learning using partially ordered sets"

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)