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: "generative steganography with kerckhoffs principle based on generative adversarial networks"
  Abstract: The distortion in steganography comes from the modification or recoding on the cover image during the embedding process. The changes of the cover always leave the steganalyzer with possibility of discriminating. Therefore, we propose to use a cover to send out secret messages without any modification by training the cover image to generate the secret messages. To ensure the security of such a generative scheme, we require the generator to meet Kerckhoffs' principle. Based on above proposal, we propose generative steganography with Kerckhoffs' principle (GSK) in this letter. In GSK, we use a generator based on generative adversarial networks (GAN) to output the secret messages if and only if the data-hiding key and the cover image are both inputted. Without the data-hiding key or the cover image, only meaningless results would be outputted. We detail the framework and the internal structure of GSK. In our training procedures, there are two GANs, Key-GAN and Cover-GAN, designed to work jointly making the generated results under the control of the data-hiding key and the cover image. Experimental results concentrate on the training process and the working performance of GSK which demonstrate that GSK can use any image to realize steganography with Kerckhoffs' principle without any modification.

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"
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "the reincarnation of grille cipher"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "passgan a deep learning approach for password guessing"
    - "learning to protect communications with adversarial neural cryptography"
    - "ssgan secure steganography based on generative adversarial networks"
    - "steganographic generative adversarial networks"
    - "semantic image inpainting with deep generative models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "generative steganography by sampling"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "ssgan secure steganography based on generative adversarial networks"
    - "steganographic generative adversarial networks"
    - "semantic image inpainting with deep generative models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "digital cardan grille a modern approach for information hiding"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "ssgan secure steganography based on generative adversarial networks"
    - "steganographic generative adversarial networks"
    - "semantic image inpainting with perceptual and contextual losses"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "steganography security principle and practice"):
  with members:
    - "semantic image inpainting with deep generative models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "texture synthesis using convolutional neural networks"

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.MM(Multimedia)