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 convolutional neural network to detect j uniward"
  Abstract: This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG steganographic method. Experiments guiding the architectural design of the CNNs have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of size 512x512. Results have verified that both the pooling method and the depth of the CNNs are critical for performance. Results have also proved that a 20-layer CNN, in general, outperforms the most sophisticated feature-based methods, but its advantage gradually diminishes on hard-to-detect cases. To show that the performance generalizes to large-scale databases and to different cover sizes, one experiment has been conducted on the CLS-LOC dataset of ImageNet containing more than one million covers cropped to unified size of 256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently proposed for large-scale JPEG steganalysis by 35%. Source code is available via GitHub: this https URL

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"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "progressive neural architecture search"
    - "ssgan secure steganography based on generative adversarial networks"
    - "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"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "generalized orderless pooling performs implicit salient matching"
    - "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"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "data driven sparse structure selection for deep neural networks"
    - "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"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "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:
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "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 "istego100k large scale image steganalysis dataset"):
  with members:
    - "real time steganalysis for stream media based on multi channel convolutional sliding windows"
    - "ts rnn text steganalysis based on recurrent neural networks"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "aag stega automatic audio generation based steganography"

Connected through hyperedge HE-7 (induced by source paper "hiding information in big data based on deep learning"):
  with members:
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "embedding watermarks into deep neural networks"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "convolutional neural network steganalysis s application to steganography"

Connected through hyperedge HE-8 (induced by source paper "universal stego post processing for enhancing image steganography"):
  with members:
    - "cnn based adversarial embedding for image steganography"
    - "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)