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 "spec resnet a general audio steganalysis scheme based on deep residual network of spectrogram"):
  Abstract: The widespread application of audio and video communication technology make the compressed audio data flowing over the Internet, and make it become an important carrier for covert communication. There are many steganographic schemes emerged in the mainstream audio compression data, such as AAC and MP3, followed by many steganalysis schemes. However, these steganalysis schemes are only effective in the specific embedded domain. In this paper, a general steganalysis scheme Spec-ResNet (Deep Residual Network of Spectrogram) is proposed to detect the steganography schemes of different embedding domain for AAC and MP3. The basic idea is that the steganographic modification of different embedding domain will all introduce the change of the decoded audio signal. In this paper, the spectrogram, which is the visual representation of the spectrum of frequencies of audio signal, is adopted as the input of the feature network to extract the universal features introduced by steganography schemes; Deep Neural Network Spec-ResNet is well-designed to represent the steganalysis feature; and the features extracted from different spectrogram windows are combined to fully capture the steganalysis features. The experiment results show that the proposed scheme has good detection accuracy and generality. The proposed scheme has better detection accuracy for three different AAC steganographic schemes and MP3Stego than the state-of-arts steganalysis schemes which are based on traditional hand-crafted or CNN-based feature. To the best of our knowledge, the audio steganalysis scheme based on the spectrogram and deep residual network is first proposed in this paper. The method proposed in this paper can be extended to the audio steganalysis of other codec or audio forensics.

Includes members:
  Member "large scale jpeg image steganalysis using hybrid deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning in steganography and steganalysis from 2015 to 2018")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")

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)