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 "synchronization detection and recovery of steganographic messages with adversarial learning"):
  Abstract: As a means for secret communication, steganography aims at concealing a message within a medium such that the presence of the hidden message can hardly be detected. In computer vision tasks, adversarial training has be-come a competitive learning method to generate images. However, the gen-erative tasks are confronted with great challenge on synthesizing images. This paper studies the mechanism of applying adversarial learning for dis-criminative tasks to learn the steganographic algorithm. We show that through unsupervised adversarial training, the adversarial model can pro-duce robust steganographic solutions, which act like an encryption. Through supervised adversarial training, we can also train a robust ste-ganalyzer, which is utilized to discriminate whether an image contains se-cret information. Our model is composed of three modules, i.e. a generator, a discriminator and a steganalyzer. All the three members are trained simulta-neously. To formulate the algorithm, we use a game to represent the com-munication between the three parties. In this game, the generator and dis-criminator attempt to communicate with each other with secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. Experimental results demonstrate the effectiveness of the proposed method on steganography tasks.

Includes members:
  Member "learning to protect communications with adversarial neural cryptography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "ssgan secure steganography based on generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "generative adversarial text to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "infogan interpretable representation learning by information maximizing generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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.CV(Computer Vision and Pattern Recognition)