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: "how image degradations affect deep cnn based face recognition"
  Abstract: Face recognition approaches that are based on deep convolutional neural networks (CNN) have been dominating the field. The performance improvements they have provided in the so called in-the-wild datasets are significant, however, their performance under image quality degradations have not been assessed, yet. This is particularly important, since in real-world face recognition applications, images may contain various kinds of degradations due to motion blur, noise, compression artifacts, color distortions, and occlusion. In this work, we have addressed this problem and analyzed the influence of these image degradations on the performance of deep CNN-based face recognition approaches using the standard LFW closed-set identification protocol. We have evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and GoogLeNet. Results have indicated that blur, noise, and occlusion cause a significant decrease in performance, while deep CNN models are found to be robust to distortions, such as color distortions and change in color balance.

Connected through hyperedge HE-1 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-2 (induced by source paper "deepcorrect correcting dnn models against image distortions"):
  with members:
    - "deflecting adversarial attacks with pixel deflection"
    - "on classification of distorted images with deep convolutional neural networks"
    - "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"
    - "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 "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"
    - "deepcorrect correcting dnn models against image distortions"
    - "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"

Connected through hyperedge HE-4 (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"
    - "on classification of distorted images with deep convolutional neural networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "understanding unconventional preprocessors in deep convolutional neural networks for face identification"):
  with members:
    - "a survey on deep transfer learning"
    - "ring loss convex feature normalization for face recognition"
    - "expressing facial structure and appearance information in frequency domain for face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "understanding how image quality affects deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "enhance visual recognition under adverse conditions via deep networks"):
  with members:
    - "understanding how image quality affects deep neural networks"
    - "studying very low resolution recognition using deep networks"
    - "learning sparse feature representations using probabilistic quadtrees and deep belief nets"
    - "wider face a face detection benchmark"
    - "deepfont identify your font from an image"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "visual psychophysics for making face recognition algorithms more explainable"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "predicting first impressions with deep learning"

Connected through hyperedge HE-8 (induced by source paper "effects of image degradations to cnn based image classification"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "real time detection tracking and classification of moving and stationary objects using multiple fisheye images"
    - "squeeze and excitation networks"
    - "dehazenet an end to end system for single image haze removal"
    - "parametric object motion from blur"
    - "studying very low resolution recognition using deep networks"
    - "the next best underwater view"
    - "accurate image super resolution using very deep convolutional 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)