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 "pixelsteganalysis pixel wise hidden information removal with low visual degradation"):
  Abstract: It is difficult to detect and remove secret images that are hidden in natural images using deep-learning algorithms. Our technique is the first work to effectively disable covert communications and transactions that use deep-learning steganography. We address the problem by exploiting sophisticated pixel distributions and edge areas of images using a deep neural network. Based on the given information, we adaptively remove secret information at the pixel level. We also introduce a new quantitative metric called destruction rate since the decoding method of deep-learning steganography is approximate (lossy), which is different from conventional steganography. We evaluate our technique using three public benchmarks in comparison with conventional steganalysis methods and show that the decoding rate improves by 10 ~ 20%.

Includes members:
  Member "spatial image steganography based on generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "invisible steganography via generative adversarial network":
    also connected through other hyperedges:
      (none)
  Member "stegnet mega image steganography capacity with deep convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "steganogan high capacity image steganography with gans")
  Member "ssgan secure steganography based on generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive density estimation for generative models")
  Member "steganographic generative adversarial networks":
    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")

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)