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 "wisernet wider separate then reunion network for steganalysis of color images"):
  Abstract: Until recently, deep steganalyzers in the spatial domain have been all designed for gray-scale images. In this paper, we propose the wider separate-then-reunion network (WISERNet) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of linear collusion attack which reserves strong correlated patterns while impairs uncorrelated noises. Therefore, in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers, we believe that the summation in normal convolution is beneficial. Therefore, we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of linear collusion attack. As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset that contains 100, 000 raw images, with different demosaicking algorithms and down-sampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deep-learning steganalyzer as far as we know, which is scarce in the literature.

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 convolutional neural network to detect j uniward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning in steganography and steganalysis from 2015 to 2018")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "π nets deep polynomial neural networks")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)