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 for source camera identification on mobile devices"
  Abstract: Abstract   In the present paper, we propose a source camera identification (SCI) method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile device cameras (MICHE-I Dataset) and the obtained results have proved the robustness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "combining prnu and noiseprint for robust and efficient device source identification"):
  with members:
    - "accurate and scalable image clustering based on sparse representation of camera fingerprint"
    - "noiseprint a cnn based camera model fingerprint"
    - "camera based image forgery localization using convolutional neural networks"
    - "first steps toward camera model identification with convolutional neural networks"
    - "a counter forensic method for cnn based camera model identification"

Connected through hyperedge HE-2 (induced by source paper "on the transferability of adversarial examples against cnn based image forensics"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "a counter forensic method for cnn based camera model identification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "first steps toward cnn based source classification of document images shared over messaging app"):
  with members:
    - "single classifier based passive system for source printer classification using local texture features"
    - "first steps toward camera model identification with convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale 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)