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: "unsupervised steganalysis based on artificial training sets"
  Abstract: In this paper, an unsupervised steganalysis method that combines artificial training sets and supervised classification is proposed. We provide a formal framework for unsupervised classification of stego and cover images in the typical situation of targeted steganalysis (i.e., for a known algorithm and approximate embedding bit rate). We also present a complete set of experiments using (1) eight different image databases, (2) image features based on Rich Models, and (3) three different embedding algorithms: Least Significant Bit (LSB) matching, Highly undetectable steganography (HUGO) and Wavelet Obtained Weights (WOW). We show that the experimental results outperform previous methods based on Rich Models in the majority of the tested cases. At the same time, the proposed approach bypasses the problem of Cover Source Mismatch - when the embedding algorithm and bit rate are known - since it removes the need of a training database when we have a large enough testing set. Furthermore, we provide a generic proof of the proposed framework in the machine learning context. Hence, the results of this paper could be extended to other classification problems similar to steganalysis.

Connected through hyperedge HE-1 (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-2 (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"
    - "steganographic generative adversarial networks"
    - "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-3 (induced by source paper "steganography using a 3 player game"):
  with members:
    - "cnn based adversarial embedding for image steganography"
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "hidden hiding data with deep networks"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "learning to protect communications with adversarial neural cryptography"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch"

Connected through hyperedge HE-4 (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"
    - "steganographic generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "hidden hiding data with deep networks"):
  with members:
    - "a robust blind watermarking using convolutional neural network"
    - "learning to protect communications with adversarial neural cryptography"
    - "generating steganographic text with lstms"
    - "embedding watermarks into deep neural networks"
    - "practical black box attacks against machine learning"
    - "adversarial examples in the physical world"
    - "adam a method for stochastic optimization"
    - "intriguing properties of 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)