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: "steganographic generative adversarial networks"
  Abstract: Steganography is collection of methods to hide secret information ("payload") within non-secret information ("container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.

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 "deep learning in steganography and steganalysis from 2015 to 2018"):
  with members:
    - "cnn based adversarial embedding for image steganography"
    - "pooled steganalysis in jpeg how to deal with the spreading strategy"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "learning to protect communications with adversarial neural cryptography"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "progressive neural architecture search"
    - "ssgan secure steganography based on generative adversarial networks"
    - "deep convolutional neural network to detect j uniward"

Connected through hyperedge HE-3 (induced by source paper "adversarial embedding a robust and elusive steganography and watermarking technique"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the adversarial attack and detection under the fisher information metric"
    - "hidden hiding data with deep networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "on the statistical detection of adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "pixelsteganalysis destroying hidden information with a low degree of visual degradation"):
  with members:
    - "spatial image steganography based on generative adversarial network"
    - "convolutional video steganography with temporal residual modeling"
    - "stegnet mega image steganography capacity with deep convolutional network"
    - "ssgan secure steganography based on generative adversarial networks"
    - "an improvement on lsb matching and lsb matching revisited steganography methods"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "unsupervised steganalysis based on artificial training sets"
    - "deep learning for steganalysis is better than a rich model with an ensemble classifier and is natively robust to the cover source mismatch"

Connected through hyperedge HE-5 (induced by source paper "pixelsteganalysis pixel wise hidden information removal with low visual degradation"):
  with members:
    - "spatial image steganography based on generative adversarial network"
    - "invisible steganography via generative adversarial network"
    - "stegnet mega image steganography capacity with deep convolutional network"
    - "ssgan secure steganography based on generative adversarial networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "unsupervised steganalysis based on artificial training sets"

Connected through hyperedge HE-6 (induced by source paper "stegnet mega image steganography capacity with deep convolutional network"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "ssgan secure steganography based on generative adversarial networks"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "xception deep learning with depthwise separable convolutions"
    - "fully convolutional instance aware semantic segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (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"
    - "generative steganography with kerckhoffs principle based on 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-8 (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"
    - "generative steganography with kerckhoffs principle based on 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"

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)