human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "clinical assistant diagnosis for electronic medical record based on convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks")
  Member "automatically generate steganographic text based on markov model and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks")
  Member "how to augment a small learning set for improving the performances of a cnn based steganalyzer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "steganography using a 3 player game")
  Member "generating steganographic text with lstms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks")
  Member "a novel convolutional neural network for image steganalysis with shared normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cis net a novel cnn model for spatial image steganalysis via cover image suppression")
  Member "image captioning with object detection and localization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "fractalnet ultra deep neural networks without residuals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bats binary architecture search")

Question: Please tell me which class the source paper 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)