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: "ssgan secure steganography based on generative adversarial networks"
  Abstract: In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.

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"
    - "adaptive spatial steganography based on probability controlled adversarial examples"
    - "generative adversarial networks an overview"
    - "an introduction to image synthesis with generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "net2vis a visual grammar for automatically generating publication ready cnn architecture visualizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "cnncomparator comparative analytics of convolutional neural networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (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"
    - "deep convolutional neural network to detect j uniward"
    - "steganographic generative adversarial networks"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "steganographic generative adversarial networks"
    - "unsupervised steganalysis based on artificial training sets"

Connected through hyperedge HE-6 (induced by source paper "hide the image in fc densenets to another image"):
  with members:
    - "steganogan high capacity image steganography with gans"
    - "hidden hiding data with deep networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-7 (induced by source paper "stegnet mega image steganography capacity with deep convolutional network"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "steganographic generative adversarial networks"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "xception deep learning with depthwise separable convolutions"
    - "fully convolutional instance aware semantic segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "invisible steganography via generative adversarial network"):
  with members:
    - "spatial image steganography based on generative adversarial network"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "end to end trained cnn encode decoder networks for image steganography"
    - "improved techniques for training gans"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.CV(Computer Vision and Pattern Recognition)