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: "paediatric bone age assessment using deep convolutional neural networks"
  Abstract: Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. This dataset consists of 12,600 radiological images. Each radiograph in the dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach introduces a comprehensive preprocessing protocol based on the positive mining technique. We use images of whole hands as well as specific hand parts for both training and prediction. This allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested methods in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

Connected through hyperedge HE-1 (induced by source paper "albumentations fast and flexible image augmentations"):
  with members:
    - "camera model identification using convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "autoaugment learning augmentation policies from data"
    - "feature pyramid network for multi class land segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "automatic whole body bone age assessment using deep hierarchical features"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "prsnet part relation and selection network for bone age assessment"):
  with members:
    - "learning to navigate for fine grained classification"
    - "residual attention based network for hand bone age assessment"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "automatic instrument segmentation in robot assisted surgery using deep learning"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "angiodysplasia detection and localization using deep convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "camera model identification using convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "feature pyramid network for multi class land segmentation"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning approaches for understanding simple speech commands"):
  with members:
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"):
  with members:
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"

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)