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: "a novel convolutional neural network for image steganalysis with shared normalization"
  Abstract: Deep learning based image steganalysis has attracted increasing attentions in recent years. Several Convolutional Neural Network (CNN) models have been proposed and achieved state-of-the-art performances on detecting steganography. In this paper, we explore an important technique in deep learning, the batch normalization, for the task of image steganalysis. Different from natural image classification, steganalysis is to discriminate cover images and stego images which are the result of adding weak stego signals into covers. This characteristic makes a cover image is more statistically similar to its stego than other cover images, requiring steganalytic methods to use paired learning to extract effective features for image steganalysis. Our theoretical analysis shows that a CNN model with multiple normalization layers is hard to be generalized to new data in the test set when it is well trained with paired learning. To hand this difficulty, we propose a novel normalization technique called Shared Normalization (SN) in this paper. Unlike the batch normalization layer utilizing the mini-batch mean and standard deviation to normalize each input batch, SN shares same statistics for all training and test batches. Based on the proposed SN layer, we further propose a novel neural network model for image steganalysis. Extensive experiments demonstrate that the proposed network with SN layers is stable and can detect the state of the art steganography with better performances than previous methods.

Connected through hyperedge HE-1 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows"):
  with members:
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "generating steganographic text with lstms"
    - "image captioning with object detection and localization"
    - "densely connected convolutional networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "bag of tricks for efficient text classification"

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"
    - "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"
    - "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 "hiding information in big data based on deep learning"):
  with members:
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "deep convolutional neural network to detect j uniward"
    - "embedding watermarks into deep neural networks"
    - "convolutional neural network steganalysis s application to steganography"

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)