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: "stegnet mega image steganography capacity with deep convolutional network"
  Abstract: Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.

Connected through hyperedge HE-1 (induced by source paper "steganogan high capacity image steganography with gans"):
  with members:
    - "hidden hiding data with deep networks"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stacked generative adversarial networks"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "energy based generative adversarial network"
    - "densely connected convolutional networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "stegastamp invisible hyperlinks in physical photographs"):
  with members:
    - "steganogan high capacity image steganography with gans"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "deep charuco dark charuco marker pose estimation"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "hidden hiding data with deep networks"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-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"
    - "ssgan secure steganography based on generative adversarial networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "steganographic generative adversarial networks"
    - "unsupervised steganalysis based on artificial training sets"

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)