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: "traffic sign classification using deep inception based convolutional networks"
  Abstract: In this work, we propose a novel deep network for traffic sign classification that achieves outstanding performance on GTSRB surpassing all previous methods. Our deep network consists of spatial transformer layers and a modified version of inception module specifically designed for capturing local and global features together. This features adoption allows our network to classify precisely intraclass samples even under deformations. Use of spatial transformer layer makes this network more robust to deformations such as translation, rotation, scaling of input images. Unlike existing approaches that are developed with hand-crafted features, multiple deep networks with huge parameters and data augmentations, our method addresses the concern of exploding parameters and augmentations. We have achieved the state-of-the-art performance of 99.81\% on GTSRB dataset.

Connected through hyperedge HE-1 (induced by source paper "deep learning for large scale traffic sign detection and recognition"):
  with members:
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "a novel plsa based traffic signs classification system"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "efficient traffic sign recognition with scale aware cnn"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "training region based object detectors with online hard example mining"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "spatial transformer networks"
    - "multi scale context aggregation by dilated convolutions"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-3 (induced by source paper "towards ophthalmologist level accurate deep learning system for oct screening and diagnosis"):
  with members:
    - "towards radiologist level accurate deep learning system for pulmonary screening"
    - "deep learning based retinal oct segmentation"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "rethinking convolutional semantic segmentation learning"
    - "improved microaneurysm detection using deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a gaussian scale space approach for exudates detection classification and severity prediction"

Connected through hyperedge HE-4 (induced by source paper "towards radiologist level accurate deep learning system for pulmonary screening"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "deep learning generalization requires deep compositional feature space design"
    - "rethinking convolutional semantic segmentation learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "rethinking convolutional semantic segmentation learning"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "deep learning generalization requires deep compositional feature space design"
    - "mask r cnn"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "wider or deeper revisiting the resnet model for visual recognition"

Connected through hyperedge HE-6 (induced by source paper "master s thesis deep learning for visual recognition"):
  with members:
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "the mythos of model interpretability"
    - "do semantic parts emerge in convolutional neural networks"
    - "weakly supervised deep detection networks"
    - "recent advances in convolutional neural networks"
    - "deep learning in neural networks"
    - "spatial transformer networks"

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)