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 in steganography and steganalysis from 2015 to 2018"
  Abstract: For almost 10 years, the detection of a message hidden in an image has been mainly carried out by the computation of a Rich Model (RM), followed by a classification by an Ensemble Classifier (EC). In 2015, the first study using a convolutional neural network (CNN) obtained the first results of steganalysis by Deep Learning approaching the results of two-step approaches (EC + RM). Therefore, over the 2015-2018 period, numerous publications have shown that it is possible to obtain better performances notably in spatial steganalysis, in JPEG steganalysis, in Selection-Channel-Aware steganalysis, in quantitative steganalysis. This chapter deals with deep learning in steganalysis from the point of view of the existing, by presenting the different neural networks that have been evaluated with a methodology specific to the discipline of steganalysis, and this during the period 2015-2018. The chapter is not intended to repeat the basic concepts of machine learning or deep learning. We will thus give in a generic way the structure of a deep neural network, we will present the networks proposed in the literature for the different scenarios of steganalysis, and finally, we will discuss steganography by GAN.

Connected through hyperedge HE-1 (induced by source paper "recent advances of image steganography with generative adversarial networks"):
  with members:
    - "steganogan high capacity image steganography with gans"
    - "cnn based adversarial embedding for image steganography"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "spatial image steganography based on generative adversarial network"
    - "adaptive spatial steganography based on probability controlled adversarial examples"
    - "generative adversarial networks an overview"
    - "an introduction to image synthesis with generative adversarial nets"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-2 (induced by source paper "steganography using a 3 player game"):
  with members:
    - "cnn based adversarial embedding for image steganography"
    - "hidden hiding data with deep networks"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "learning to protect communications with adversarial neural cryptography"
    - "unsupervised steganalysis based on artificial training sets"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch"

Connected through hyperedge HE-3 (induced by source paper "hiding information in big data based on deep learning"):
  with members:
    - "deep convolutional neural network to detect j uniward"
    - "embedding watermarks into deep neural networks"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "convolutional neural network steganalysis s application to steganography"

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.CR(Cryptography and Security)