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: "effects of degradations on deep neural network architectures"
  Abstract: Deep convolutional neural networks (CNNs) have achieved many state-of-the-art results recently in different fields of computer vision. Different architectures are emerging almost everyday that produce fascinating results in machine vision tasks consisting million of images. Though CNN based models are able to detect and recognize large number of object classes, most of the times these models are trained with high quality images. Recently, an alternative architecture for image classification is proposed by Sabour et al. \cite{sabour2017dynamic} based on groups of neurons (capsules) and a novel dynamic routing protocol. The architecture has shown promising result by surpassing the performances of state-of-the-art CNN based models in some of the existing datasets. However, the behavior of capsule based models and CNN based models are largely unknown in presence of noise. As in most of the practical applications, it can not be guaranteed the input image is completely noise-free and does not contain any distortion, it is important to study the performance of these models under different noise conditions. In this paper, we select six widely used CNN architectures and consider their performances for image classification task for two different datasets under various image quality distortions. We show that the capsule network is more robust to several image degradations than the CNN based models. To the best of our knowledge, this is the first study on the performance of CapsuleNet (or CapsNet) and other state-of-the-art CNN architectures under different types of image degradations.

Connected through hyperedge HE-1 (induced by source paper "a possible reason for why data driven beats theory driven computer vision"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "leverage eye movement data for saliency modeling invariance analysis and a robust new model"
    - "yolov3 an incremental improvement"
    - "generalisation in humans and deep neural networks"
    - "the elephant in the room"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "active control of camera parameters for object detection algorithms"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks"):
  with members:
    - "generalized capsule networks with trainable routing procedure"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "an artificial neural network architecture based on context transformations in cortical minicolumns"
    - "snapshot ensembles train 1 get m for free"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "cross domain image classification through neural style transfer data augmentation"):
  with members:
    - "a survey on face data augmentation"
    - "data augmentation using gans"
    - "stada style transfer as data augmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "style augmentation data augmentation via style randomization"
    - "domain adaptation for visual applications a comprehensive survey"
    - "demystifying neural style transfer"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-4 (induced by source paper "extracting interpretable concept based decision trees from cnns"):
  with members:
    - "interpreting cnns via decision trees"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image 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)