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: "deepcorrect correcting dnn models against image distortions"
  Abstract: In recent years, the widespread use of deep neural networks (DNNs) has facilitated great improvements in performance for computer vision tasks like image classification and object recognition. In most realistic computer vision applications, an input image undergoes some form of image distortion such as blur and additive noise during image acquisition or transmission. Deep networks trained on pristine images perform poorly when tested on such distortions. In this paper, we evaluate the effect of image distortions like Gaussian blur and additive noise on the activations of pre-trained convolutional filters. We propose a metric to identify the most noise susceptible convolutional filters and rank them in order of the highest gain in classification accuracy upon correction. In our proposed approach called  DeepCorrect , we apply small stacks of convolutional layers with  residual connections  at the output of these ranked filters and train them to correct the worst distortion affected filter activations, while leaving the rest of the pre-trained filter outputs in the network unchanged. Performance results show that applying  DeepCorrect  models for common vision tasks like image classification (ImageNet), object recognition (Caltech-101, Caltech-256), and scene classification (SUN-397), significantly improves the robustness of DNNs against distorted images and outperforms other alternative approaches.

Connected through hyperedge HE-1 (induced by source paper "benchmarking the robustness of semantic segmentation models"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "semantic foggy scene understanding with synthetic data"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-2 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"
    - "divide denoise and defend against adversarial attacks"

Connected through hyperedge HE-3 (induced by source paper "it gan do better gan based detection of objects on images with varying quality"):
  with members:
    - "a robust learning approach to domain adaptive object detection"
    - "focal loss for dense object detection"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "perceptual generative adversarial networks for small object detection"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "how image degradations affect deep cnn based face recognition"

Connected through hyperedge HE-4 (induced by source paper "improving image classification robustness through selective cnn filters fine tuning"):
  with members:
    - "exploring generalization in deep learning"
    - "on classification of distorted images with deep convolutional neural networks"
    - "examining the impact of blur on recognition by convolutional networks"
    - "understanding how image quality affects deep neural networks"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-5 (induced by source paper "generative sensing transforming unreliable sensor data for reliable recognition"):
  with members:
    - "can the early human visual system compete with deep neural networks"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "infrared colorization using deep convolutional neural networks"
    - "understanding how image quality affects deep neural networks"
    - "colorful image colorization"
    - "a neural algorithm of artistic style"

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)