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: "cnn based adversarial embedding for image steganography"
  Abstract: Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML)-based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artifacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding (ADV-EMB), which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN)-based steganalyzer. The proposed method works under the conventional framework of distortion minimization. In particular, ADV-EMB adjusts the costs of image elements modifications according to the gradients back propagated from the target CNN steganalyzer. Therefore, modification direction has a higher probability to be the same as the inverse sign of the gradient. In this way, the so-called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme achieves better security performance against the target adversary-unaware steganalyzer by increasing its missed detection rate. In addition, it deteriorates the performance of other adversary-aware steganalyzers, opening the way to a new class of modern steganographic schemes capable of overcoming powerful CNN-based steganalysis.

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"
    - "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"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-2 (induced by source paper "neural imaging pipelines the scourge or hope of forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stereoscopic dark flash for low light photography"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning rich features for image manipulation detection"
    - "synthetic depth of field with a single camera mobile phone"
    - "do gans leave artificial fingerprints"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "content authentication for neural imaging pipelines end to end optimization of photo provenance in complex distribution channels"

Connected through hyperedge HE-3 (induced by source paper "deep learning in steganography and steganalysis from 2015 to 2018"):
  with members:
    - "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"
    - "ssgan secure steganography based on generative adversarial networks"
    - "deep convolutional neural network to detect j uniward"
    - "steganographic generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "steganography using a 3 player game"):
  with members:
    - "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"
    - "deep learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch"

Connected through hyperedge HE-5 (induced by source paper "cnn based steganalysis and parametric adversarial embedding a game theoretic framework"):
  with members:
    - "adversarial source identification game with corrupted training"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a game theoretic framework for optimum decision fusion in the presence of byzantines"
    - "binary hypothesis testing game with training data"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "enhancing jpeg steganography using iterative adversarial examples"):
  with members:
    - "weakening the detecting capability of cnn based steganalysis"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "universal stego post processing for enhancing image steganography"):
  with members:
    - "deep convolutional neural network to detect j uniward"
    - "intriguing properties of neural networks"

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)