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: "large scale jpeg image steganalysis using hybrid deep learning framework"
  Abstract: Adoption of deep learning in image steganalysis is still in its initial stage. In this paper, we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. Our proposed framework involves two main stages. The first stage is hand-crafted, corresponding to the convolution phase and the quantization and truncation phase of the rich models. The second stage is a compound deep-neural network containing multiple deep subnets, in which the model parameters are learned in the training procedure. We provided experimental evidence and theoretical reflections to argue that the introduction of threshold quantizers, though disabling the gradient-descent-based learning of the bottom convolution phase, is indeed cost-effective. We have conducted extensive experiments on a large-scale data set extracted from ImageNet. The primary data set used in our experiments contains 500 000 cover images, while our largest data set contains five million cover images. Our experiments show that the integration of quantization and truncation into deep-learning steganalyzers do boost the detection performance by a clear margin. Furthermore, we demonstrate that our framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different data set. These properties are of critical importance in practical applications.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "calpa net channel pruning assisted deep residual network for steganalysis of digital images"):
  with members:
    - "wisernet wider separate then reunion network for steganalysis of color images"
    - "rethinking the value of network pruning"
    - "data driven sparse structure selection for deep neural networks"
    - "deep convolutional neural network to detect j uniward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"

Connected through hyperedge HE-4 (induced by source paper "cnn based adversarial embedding for image steganography"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "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 learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-5 (induced by source paper "wisernet wider separate then reunion network for steganalysis of color images"):
  with members:
    - "deep convolutional neural network to detect j uniward"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "spec resnet a general audio steganalysis scheme based on deep residual network of spectrogram"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "deep learning in information security"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "independently controllable factors"
    - "magnet a two pronged defense against adversarial examples"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "a secure mobile authentication alternative to biometrics"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "few shot learning through an information retrieval lens"

Connected through hyperedge HE-8 (induced by source paper "enabling trust in deep learning models a digital forensics case study"):
  with members:
    - "cloudme forensics a case of big data forensic investigation"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"
    - "axiomatic attribution for deep networks"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"

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)