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: "deep learning based large scale automatic satellite crosswalk classification"
  Abstract: High-resolution satellite imagery has been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Despite the high availability, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this data set is used to train deep-learning-based models in order to accurately classify satellite images that contain or not contain zebra crossings. A novel data set with more than 240000 images from 3 continents, 9 countries, and more than 20 cities was used in the experiments. The experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.

Connected through hyperedge HE-1 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "generative adversarial networks for black box api attacks with limited training data"
    - "adversarial active learning for deep networks a margin based approach"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-2 (induced by source paper "cross domain car detection using unsupervised image to image translation from day to night"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "on convergence and stability of gans"
    - "pixel and feature level based domain adaption for object detection in autonomous driving"
    - "which training methods for gans do actually converge"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "effortless deep training for traffic sign detection using templates and arbitrary natural images"):
  with members:
    - "self driving cars a survey"
    - "self paced deep learning for weakly supervised object detection"
    - "generative one shot learning gol a semi parametric approach to one shot learning in autonomous vision"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "few shot object detection via feature reweighting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstd a low shot transfer detector for object detection"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"

Connected through hyperedge HE-4 (induced by source paper "traffic light recognition using deep learning and prior maps for autonomous cars"):
  with members:
    - "self driving cars a survey"
    - "a hierarchical deep architecture and mini batch selection method for joint traffic sign and light detection"
    - "yolov3 an incremental improvement"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "copycat cnn stealing knowledge by persuading confession with random non labeled data"):
  with members:
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "efficient defenses against adversarial attacks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"):
  with members:
    - "ego lane analysis system elas dataset and algorithms"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"

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)