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: "quality resilient deep neural networks"
  Abstract: We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networks when tested on distorted data. However, fine-tuned networks perform poorly on quality distortions that they have not been trained for. We propose a mixture of experts ensemble method that is robust to different types of distortions. The "experts" in our model are trained on a particular type of distortion. The output of the model is a weighted sum of the expert models, where the weights are determined by a separate gating network. The gating network is trained to predict optimal weights for a particular distortion type and level. During testing, the network is blind to the distortion level and type, yet can still assign appropriate weights to the expert models. We additionally investigate weight sharing methods for the mixture model and show that improved performance can be achieved with a large reduction in the number of unique network parameters.

Connected through hyperedge HE-1 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"

Connected through hyperedge HE-2 (induced by source paper "trained model fusion for object detection using gating network"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "yolov3 an incremental improvement"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "partial adversarial domain adaptation"
    - "edf ensemble distill and fuse for easy video labeling"
    - "robust deep multi modal learning based on gated information fusion network"
    - "rank of experts detection network ensemble"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (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-4 (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"
    - "cure or challenging unreal and real environments for object recognition"
    - "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"

Connected through hyperedge HE-5 (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"
    - "cure or challenging unreal and real environments for object recognition"
    - "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"

Connected through hyperedge HE-6 (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"
    - "an empirical study on the effects of different types of noise in image classification tasks"
    - "enriching word vectors with subword information"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-7 (induced by source paper "towards robust neural networks with lipschitz continuity"):
  with members:
    - "on classification of distorted images with deep convolutional neural networks"
    - "residual attention network for image classification"
    - "examining the impact of blur on recognition by convolutional networks"
    - "towards robust deep neural networks with bang"
    - "identity mappings in deep residual networks"
    - "understanding how image quality affects deep neural networks"
    - "improving the robustness of deep neural networks via stability training"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "google s cloud vision api is not robust to noise"):
  with members:
    - "deceiving google s perspective api built for detecting toxic comments"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "deceiving google s cloud video intelligence api built for summarizing videos"
    - "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"
    - "how image degradations affect deep cnn based face recognition"
    - "concrete problems in ai safety"

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)