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: "on classification of distorted images with deep convolutional neural networks"
  Abstract: Image blur and image noise are common distortions during image acquisition. In this paper, we systematically study the effect of image distortions on the deep neural network (DNN) image classifiers. First, we examine the DNN classifier performance under four types of distortions. Second, we propose two approaches to alleviate the effect of image distortion: re-training and fine-tuning with noisy images. Our results suggest that, under certain conditions, fine-tuning with noisy images can alleviate much effect due to distorted inputs, and is more practical than re-training.

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"
    - "deepcorrect correcting dnn models against image distortions"
    - "semantic foggy scene understanding with synthetic data"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "deepcorrect correcting dnn models against image distortions"):
  with members:
    - "deflecting adversarial attacks with pixel deflection"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "practical black box attacks against machine learning"
    - "how image degradations affect deep cnn based face recognition"
    - "fine grained recognition in the noisy wild sensitivity analysis of convolutional neural networks approaches"
    - "a neural approach to blind motion deblurring"

Connected through hyperedge HE-3 (induced by source paper "are adversarial robustness and common perturbation robustness independant attributes"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "spatially transformed adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "quality resilient deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "training with the invisibles obfuscating images to share safely for learning visual recognition models"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "toward multimodal image to image translation"

Connected through hyperedge HE-5 (induced by source paper "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"):
  with members:
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "cure or challenging unreal and real environments for object recognition"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "google s cloud vision api is not robust to noise"

Connected through hyperedge HE-6 (induced by source paper "improving image classification robustness through selective cnn filters fine tuning"):
  with members:
    - "deepcorrect correcting dnn models against image distortions"
    - "exploring generalization in deep learning"
    - "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-7 (induced by source paper "generalisation in humans and deep neural networks"):
  with members:
    - "totally looks like how humans compare compared to machines"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "exploring generalization in deep learning"
    - "can the early human visual system compete with deep neural networks"
    - "human perception in computer vision"
    - "opening the black box of deep neural networks via information"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-8 (induced by source paper "distortion robust image classification using deep convolutional neural network with discrete cosine transform"):
  with members:
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "universal adversarial perturbations"
    - "understanding how image quality affects deep neural networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"

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)