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: "deep learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch"
  Abstract: Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best " shape " of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the impressive results we obtained.

Connected through hyperedge HE-1 (induced by source paper "cis net a novel cnn model for spatial image steganalysis via cover image suppression"):
  with members:
    - "deep steganalysis end to end learning with supervisory information beyond class labels"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "generalized orderless pooling performs implicit salient matching"
    - "deep convolutional neural network to detect j uniward"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "improved relation classification by deep recurrent neural networks with data augmentation"

Connected through hyperedge HE-2 (induced by source paper "detecting colorized images via convolutional neural networks toward high accuracy and good generalization"):
  with members:
    - "fake colorized image detection"
    - "first steps toward camera model identification with convolutional neural networks"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "learning representations for automatic colorization"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "colorful image colorization"

Connected through hyperedge HE-3 (induced by source paper "cnn based adversarial embedding for image steganography"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "deep convolutional neural network to detect j uniward"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples in the physical world"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-4 (induced by source paper "steganography using a 3 player game"):
  with members:
    - "cnn based adversarial embedding for image steganography"
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "hidden hiding data with deep networks"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "learning to protect communications with adversarial neural cryptography"
    - "unsupervised steganalysis based on artificial training sets"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "yedrouj net an efficient cnn for spatial steganalysis"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "caffe convolutional architecture for fast feature embedding"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "how to augment a small learning set for improving the performances of a cnn based steganalyzer"):
  with members:
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "deep convolutional neural network to detect j uniward"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "steganographic generative adversarial networks"):
  with members:
    - "on unifying deep generative models"
    - "generative multi adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "deep learning for steganalysis is better than a rich model with an ensemble classifier and is natively robust to the cover source mismatch"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "improving blind steganalysis in spatial domain using a criterion to choose the appropriate steganalyzer between cnn and srm ec"):
  with members:
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key"
    - "deep learning in neural networks"
    - "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.MM(Multimedia)