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: "dual tree wavelet scattering network with parametric log transformation for object classification"
  Abstract: We introduce a ScatterNet that uses a parametric log transformation with Dual-Tree complex wavelets to extract translation invariant representations from a multi-resolution image. The parametric transformation aids the OLS pruning algorithm by converting the skewed distributions into relatively mean-symmetric distributions while the Dual-Tree wavelets improve the computational efficiency of the network. The proposed network is shown to outperform Mallat's ScatterNet on two image datasets, both for classification accuracy and computational efficiency. The advantages of the proposed network over other supervised and some unsupervised methods are also presented using experiments performed on different training dataset sizes.

Connected through hyperedge HE-1 (induced by source paper "a learnable scatternet locally invariant convolutional layers"):
  with members:
    - "deep learning in the wavelet domain"
    - "scaling the scattering transform deep hybrid networks"
    - "visualizing and improving scattering networks"
    - "dynamic steerable blocks in deep residual networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation"):
  with members:
    - "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation"
    - "discriminative training of deep fully connected continuous crfs with task specific loss"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "multi path feedback recurrent neural network for scene parsing"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "eye in the sky real time drone surveillance system dss for violent individuals identification using scatternet hybrid deep learning network"):
  with members:
    - "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation"
    - "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "feature pyramid networks for object detection"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "flowing convnets for human pose estimation in videos"
    - "robust optimization for deep regression"

Connected through hyperedge HE-4 (induced by source paper "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"):
  with members:
    - "visualizing and improving scattering networks"
    - "wide residual networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "learning from synthetic data using a stacked multichannel autoencoder"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "deep roto translation scattering for object classification"
    - "deep epitomic convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "pcanet a simple deep learning baseline for image classification"
    - "deep roto translation scattering for object classification"

Connected through hyperedge HE-6 (induced by source paper "scatternet hybrid deep learning shdl network for object classification"):
  with members:
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "improved techniques for training gans"
    - "wide residual networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "unsupervised feature learning by deep sparse coding"
    - "network in network"

Connected through hyperedge HE-7 (induced by source paper "visualizing and improving scattering networks"):
  with members:
    - "scaling the scattering transform deep hybrid networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "very deep convolutional networks for large scale image recognition"
    - "deep roto translation scattering for object classification"
    - "visualizing and understanding convolutional networks"
    - "invariant scattering convolution 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)