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: "an empirical study on the effects of different types of noise in image classification tasks"
  Abstract: Image classification is one of the main research problems in computer vision and machine learning. Since in most real-world image classification applications there is no control over how the images are captured, it is necessary to consider the possibility that these images might be affected by noise (e.g. sensor noise in a low-quality surveillance camera). In this paper we analyse the impact of three different types of noise on descriptors extracted by two widely used feature extraction methods (LBP and HOG) and how denoising the images can help to mitigate this problem. We carry out experiments on two different datasets and consider several types of noise, noise levels, and denoising methods. Our results show that noise can hinder classification performance considerably and make classes harder to separate. Although denoising methods were not able to reach the same performance of the noise-free scenario, they improved classification results for noisy data.

Connected through hyperedge HE-1 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples"):
  with members:
    - "protecting jpeg images against adversarial attacks"
    - "adversarial robustness toolbox v1 0 0"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "exploiting the inherent limitation of l0 adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "survey of face detection on low quality images"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "s 3 fd single shot scale invariant face detector"
    - "detecting faces using region based fully convolutional networks"
    - "face detection through scale friendly deep convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-4 (induced by source paper "one shot item search with multimodal data"):
  with members:
    - "deepstyle multimodal search engine for fashion and interior design"
    - "web scale responsive visual search at bing"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "visual search at ebay"
    - "visual discovery at pinterest"
    - "quality resilient deep neural networks"
    - "enriching word vectors with subword information"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-5 (induced by source paper "classification driven dynamic image enhancement"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "deep class aware denoising"
    - "fast image processing with fully convolutional networks"
    - "examining the impact of blur on recognition by convolutional networks"
    - "how image degradations affect deep cnn based face recognition"
    - "places an image database for deep scene understanding"
    - "does v nir based image enhancement come with better features"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-6 (induced by source paper "enhancing the performance of convolutional neural networks on quality degraded datasets"):
  with members:
    - "on classification of distorted images with deep convolutional neural networks"
    - "quality resilient deep neural networks"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "understanding how image quality affects deep neural networks"
    - "improving the robustness of deep neural networks via stability training"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "dirty pixels optimizing image classification architectures for raw sensor data"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "examining the impact of blur on recognition by convolutional networks"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a neural approach to blind motion deblurring"
    - "understanding how image quality affects deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "improving robustness of feature representations to image deformations using powered convolution in cnns"):
  with members:
    - "understanding how image quality affects deep neural networks"
    - "a review paper noise models in digital image processing"

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)