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: "approximating cnns with bag of local features models works surprisingly well on imagenet"
  Abstract: Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 33 x 33 px features and Alexnet performance for 17 x 17 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "momentum contrast for unsupervised visual representation learning"
    - "using self supervised learning can improve model robustness and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-3 (induced by source paper "adversarial data encryption"):
  with members:
    - "convolutional networks with dense connectivity"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial music real world audio adversary against wake word detection system"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-4 (induced by source paper "overinterpretation reveals image classification model pathologies"):
  with members:
    - "gradmask reduce overfitting by regularizing saliency"
    - "a benchmark for interpretability methods in deep neural networks"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "underwhelming generalization improvements from controlling feature attribution"
    - "probing neural network comprehension of natural language arguments"
    - "pytorch an imperative style high performance deep learning library"
    - "pathologies of neural models make interpretations difficult"

Connected through hyperedge HE-5 (induced by source paper "on the texture bias for few shot cnn segmentation"):
  with members:
    - "a new local transformation module for few shot segmentation"
    - "meta learning initializations for image segmentation"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "texture bias of cnns limits few shot classification performance"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "differentiable meta learning model for few shot semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "localizing multi scale semantic patches for image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "saccader improving accuracy of hard attention models for vision"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "searching for mobilenetv3"
    - "gated convolutional networks with hybrid connectivity for image classification"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "cbam convolutional block attention module"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-7 (induced by source paper "analyzing the dependency of convnets on spatial information"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deformable convnets v2 more deformable better results"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"
    - "deformable convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "xcos an explainable cosine metric for face verification task"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "improving the interpretability of deep neural networks with knowledge distillation"
    - "ted teaching ai to explain its decisions"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

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)