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: "cure or challenging unreal and real environments for object recognition"
  Abstract: In this paper, we introduce a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition (CURE-OR). In this dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture that are positioned in five different orientations and captured using five devices including a webcam, a DSLR, and three smartphone cameras in real-world (real) and studio (unreal) environments. The controlled challenging conditions include underexposure, overexposure, blur, contrast, dirty lens, image noise, resizing, and loss of color information. We utilize CURE-OR dataset to test recognition APIs-Amazon Rekognition and Microsoft Azure Computer Vision- and show that their performance significantly degrades under challenging conditions. Moreover, we investigate the relationship between object recognition and image quality and show that objective quality algorithms can estimate recognition performance under certain photometric challenging conditions. The dataset is publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "benchmarking neural network robustness to common corruptions and perturbations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial spheres"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "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-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"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "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"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "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"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "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"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "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 "benchmarking neural network robustness to common corruptions and surface variations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "adversarial spheres"
    - "deep anomaly detection with outlier exposure"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)