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: "examining the impact of blur on recognition by convolutional networks"
  Abstract: State-of-the-art algorithms for semantic visual tasks---such as image classification and semantic segmentation---are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annotated datasets of high-quality images that are free of artifacts. In this paper, we investigate the effect of one such artifact that is quite common in natural capture settings---blur. We show that standard pre-trained network models suffer a significant degradation in performance when applied to blurred images. We investigate the extent to which this degradation is due to the mismatch between training and input image statistics. Specifically, we find that fine-tuning a pre-trained model with blurred images added to the training set allows it to regain much of the lost accuracy. By considering different combinations of sharp and blurred images in the training set, we characterize how much degradation is caused by loss of information, and how much by the uncertainty of not knowing the nature and magnitude of blur. We find that by fine-tuning on a diverse mix of blurred images, convolutional neural networks can in fact learn to generate a blur invariant representation in their hidden layers. Broadly, our results provide practitioners with useful insights for developing vision systems that perform reliably on real world images affected by blur.

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 "natural adversarial examples"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "towards neural networks that provably know when they don t know"
    - "verified uncertainty calibration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "detecting semantic anomalies"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-3 (induced by source paper "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "dataset distillation"
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-4 (induced by source paper "achieving robustness in the wild via adversarial mixing with disentangled representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "natural adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "do imagenet classifiers generalize to imagenet"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"

Connected through hyperedge HE-5 (induced by source paper "benchmarking robustness in object detection autonomous driving when winter is coming"):
  with members:
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "rain removal in traffic surveillance does it matter"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fixing the train test resolution discrepancy"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-6 (induced by source paper "revealing scenes by inverting structure from motion reconstructions"):
  with members:
    - "privacy preserving image based localization"
    - "learning privacy preserving encodings through adversarial training"
    - "deep multi scale architectures for monocular depth estimation"
    - "semi parametric image synthesis"
    - "megadepth learning single view depth prediction from internet photos"
    - "image manipulation with perceptual discriminators"
    - "estimating depth from rgb and sparse sensing"
    - "smart sparse contours to represent and edit images"

Connected through hyperedge HE-7 (induced by source paper "orthogonal deep neural networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the singular values of convolutional layers"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "on the effect of pooling on the geometry of representations"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-8 (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"
    - "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"

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)