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 "steganography using a 3 player game"):
  Abstract: Image steganography aims to securely embed secret information into cover images. Until now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, are among the most secure and most used methods for image steganography. With the arrival of deep learning and more specifically the Generative Adversarial Networks (GAN), new techniques have appeared. Among these techniques, there is the 3 player game approaches, where three networks compete against each other.% to learn to do each Alice and Bob and jobs. In this paper, we propose three different architectures based on the 3 player game. The first-architecture is proposed as a rigorous alternative to two recent publications. The second takes into account stego noise power. Finally, our third architecture enriches the second one with a better interaction between the embedding and extracting networks. Our method achieves better results compared to the existing works GSIVAT, HiDDeN, and paves the way for future research on this topic.

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 "deep learning in steganography and steganalysis from 2015 to 2018":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "hidden hiding data with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distortion agnostic deep watermarking")
  Member "how to augment a small learning set for improving the performances of a cnn based steganalyzer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows")
  Member "learning to protect communications with adversarial neural cryptography":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "unsupervised steganalysis based on artificial training sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning in steganography and steganalysis from 2015 to 2018")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.MM(Multimedia)