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: "adaptive spatial steganography based on probability controlled adversarial examples"
  Abstract: Deep learning model is vulnerable to adversarial attack, which generates special input sample to the deep learning model that can make the model misclassify the sample. Besides deep learning model, adversarial attack is also effective to feature-based machine learning model. In this paper, we discuss the application of adversarial attack for improving the capability to resist steganalysis of steganographic schemes. We apply the steganalytic neural network as the adversarial generator. Our goal is to improve the performance of typical spatial adaptive steganography towards the steganalysis of rich model feature and neural network. The adversarial method can be combined with adaptive steganography by controlling the flipping directions of pixels following the gradient map. The steganographic adversarial example can make itself "seems like" innocent cover towards steganalyzer. However, the generated steganographic adversarial examples are only effective to deceive the steganalyzer trained with non-adversarial examples. When facing the steganalyzer trained with adversarial examples, they can be easily detected with a low detecting error rate. The adversarial method makes the generated stego image more distinguishable from the cover image. To improve this situation, we adjust the method to calculate the gradient map by modifying the probability vector of softmax layer to a particular vector rather than modifying the category vector. Therefore, the generated adversarial example is controlled by probability output of softmax. With the adjustment, the adversarial scheme performs better than the typical adaptive steganography. We develop an practical adversarial steganographic method with double-layered STC. The experiment proves its effectiveness on rich model and neural network.

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"
    - "generative adversarial networks an overview"
    - "an introduction to image synthesis with generative adversarial nets"
    - "sem gan semantically consistent image to image translation"

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)