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 multi branch convolutional neural network for detecting double jpeg compression"
  Abstract: Detection of double JPEG compression is important to forensics analysis. A few methods were proposed based on convolutional neural networks (CNNs). These methods only accept inputs from pre-processed data, such as histogram features and/or decompressed images. In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input. Considering the DCT sub-band nature in JPEG, a multiple-branch CNN structure has been designed to reveal whether a JPEG format image has been doubly compressed. Comparing with previous methods, the proposed method provides end-to-end detection capability. Extensive experiments have been carried out to demonstrate the effectiveness of the proposed network.

Connected through hyperedge HE-1 (induced by source paper "exploring semantic segmentation on the dct representation"):
  with members:
    - "harmonic networks with limited training samples"
    - "fast object detection in compressed jpeg images"
    - "dct domain deep convolutional neural networks for multiple jpeg compression classification"
    - "deep residual learning in the jpeg transform domain"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "towards image understanding from deep compression without decoding"
    - "compressed video action recognition"

Connected through hyperedge HE-2 (induced by source paper "harmonic networks with limited training samples"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "gabor convolutional networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-3 (induced by source paper "double jpeg compression detection by exploring the correlations in dct domain"):
  with members:
    - "dct domain deep convolutional neural networks for multiple jpeg compression classification"
    - "aligned and non aligned double jpeg detection using convolutional 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.CV(Computer Vision and Pattern Recognition)