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: "real time steganalysis for stream media based on multi channel convolutional sliding windows"
  Abstract: Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows. In order to analyze the correlations between frames and different neighborhood frames in a VoIP signal, we define multi channel sliding detection windows. Within each sliding window, we design two feature extraction channels which contain multiple convolution layers with multiple convolution kernels each layer to extract correlation features of the input signal. Then based on these extracted features, we use a forward fully connected network for feature fusion. Finally, by analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not.We designed several experiments to test the proposed model's detection ability under various conditions, including different embedding rates, different speech length, etc. Experimental results showed that the proposed model outperforms all the previous methods, especially in the case of low embedding rate, which showed state-of-the-art performance. In addition, we also tested the detection efficiency of the proposed model, and the results showed that it can achieve almost real-time detection of VoIP speech signals.

Connected through hyperedge HE-1 (induced by source paper "hierarchical representation network for steganalysis of qim steganography in low bit rate speech signals"):
  with members:
    - "fcem a novel fast correlation extract model for real time steganalysis of voip stream via multi head attention"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks"):
  with members:
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "generating steganographic text with lstms"
    - "image captioning with object detection and localization"
    - "neural responding machine for short text conversation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "behavioral security in covert communication systems"):
  with members:
    - "ts rnn text steganalysis based on recurrent neural networks"
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "lost in the digital wild hiding information in digital activities"
    - "aag stega automatic audio generation based steganography"

Connected through hyperedge HE-4 (induced by source paper "istego100k large scale image steganalysis dataset"):
  with members:
    - "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"
    - "deep convolutional neural network to detect j uniward"

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)