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 signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
  Abstract: Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over existing databases that mostly lack severe challenging conditions. VIP Cup 2017 competition focused on detecting such traffic signs under challenging conditions. To facilitate such task and competition, we introduced a video dataset denoted as CURE-TSD that includes a variety of challenging conditions. The goal of this challenge was to implement traffic sign detection algorithms that can robustly perform under such challenging conditions. In this article, we share an overview of the VIP Cup 2017 experience including competition setup, teams, technical approaches, participation statistics, and competition experience through finalist teams members' and organizers' eyes.

Connected through hyperedge HE-1 (induced by source paper "using videos to evaluate image model robustness"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "adversarial logit pairing"
    - "autoaugment learning augmentation policies from data"
    - "generalisation in humans and deep neural networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning transferable architectures for scalable image recognition"
    - "exploring the landscape of spatial robustness"

Connected through hyperedge HE-2 (induced by source paper "challenging environments for traffic sign detection reliability assessment under inclement conditions"):
  with members:
    - "distorted representation space characterization through backpropagated gradients"
    - "traffic sign detection under challenging conditions a deeper look into performance variations and spectral characteristics"
    - "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"
    - "cure or challenging unreal and real environments for object recognition"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "traffic sign detection under challenging conditions a deeper look into performance variations and spectral characteristics"):
  with members:
    - "distorted representation space characterization through backpropagated gradients"
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"
    - "cure or challenging unreal and real environments for object recognition"
    - "knowledge based recurrent attentive neural network for traffic sign detection"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

Connected through hyperedge HE-4 (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"
    - "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"
    - "on classification of distorted images with deep convolutional neural networks"
    - "google s cloud vision api is not robust to noise"

Connected through hyperedge HE-5 (induced by source paper "implicit background estimation for semantic segmentation"):
  with members:
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "deep anomaly detection with outlier exposure"
    - "cure or challenging unreal and real environments for object recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "on calibration of modern neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "distorted representation space characterization through backpropagated gradients"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "perceptual image quality assessment through spectral analysis of error representations"
    - "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "unique unsupervised image quality estimation"
    - "csv image quality assessment based on color structure and visual system"

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)