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 "hide the image in fc densenets to another image"):
  Abstract: In the past, steganography was to embed text in a carrier, the sender Alice and the recipient Bob share the key, and the text is extracted by Bob through the key. If more information is embedded, the image is easily distorted. In contrast, if there is less embedded information, the image maintains good visual integrity, but does not meet our requirements for steganographic capacity. In this paper, we focus on tackling these challenges and limitations to improve steganographic capacity. An image steganography method based on Fully Convolutional Dense Network(FC-DenseNet) was proposed by us. The hidden network and the extracted network are trained at the same time. The dataset of the deep neural network is derived from various natural images of ImageNet. The experimental results show that the stego-image after steganography and the secret image extracted from stego-imge have a visually good effect, and the stego-image has high capacity and high peak signal to noise ratio. Image-to-image full size hiding is implemented.

Includes members:
  Member "steganogan high capacity image steganography with gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distortion agnostic deep watermarking")
  Member "hidden hiding data with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distortion agnostic deep watermarking")
  Member "ssgan secure steganography based on generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "pyramid scene parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")

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.MM(Multimedia)