human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "cnn based adversarial embedding for image steganography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "pooled steganalysis in jpeg how to deal with the spreading strategy":
    also connected through other hyperedges:
      (none)
  Member "strike with a pose neural networks are easily fooled by strange poses of familiar objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "learning to protect communications with adversarial neural cryptography":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "large scale jpeg image steganalysis using hybrid deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cis net a novel cnn model for spatial image steganalysis via cover image suppression")
  Member "progressive neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "ssgan secure steganography based on generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "deep convolutional neural network to detect j uniward":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cis net a novel cnn model for spatial image steganalysis via cover image suppression")

Question: Please tell me which class the source paper 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)