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: "scatternet hybrid deep learning shdl network for object classification"
  Abstract: The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that extracts invariant and discriminative image representations for object recognition. SHDL framework is constructed with a multi-layer ScatterNet front-end, an unsupervised learning middle, and a supervised learning back-end module. Each layer of the SHDL network is automatically designed as an explicit optimization problem leading to an optimal deep learning architecture with improved computational performance as compared to the more usual deep network architectures. SHDL network produces the state-of-the-art classification performance against unsupervised and semi-supervised learning (GANs) on two image datasets. Advantages of the SHDL network over supervised methods (NIN, VGG) are also demonstrated with experiments performed on training datasets of reduced size.

Connected through hyperedge HE-1 (induced by source paper "multi path learnable wavelet neural network for image classification"):
  with members:
    - "deep learning in the wavelet domain"
    - "detailed dense inference with convolutional neural networks via discrete wavelet transform"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "multi level wavelet cnn for image restoration"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"
    - "wavelet convolutional neural networks for texture classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning using pretrained cnn and associative memory bank"):
  with members:
    - "dense associative memory is robust to adversarial inputs"
    - "dynamic routing between capsules"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "dense associative memory for pattern recognition"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "simultaneous deep transfer across domains and tasks"

Connected through hyperedge HE-3 (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"
    - "dual tree wavelet scattering network with parametric log transformation 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-4 (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"
    - "dual tree wavelet scattering network with parametric log transformation 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-5 (induced by source paper "deep learning in the wavelet domain"):
  with members:
    - "detailed dense inference with convolutional neural networks via discrete wavelet transform"
    - "scaling the scattering transform deep hybrid networks"
    - "visualizing and improving scattering networks"
    - "wavelet convolutional neural networks for texture classification"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-6 (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"
    - "dual tree wavelet scattering network with parametric log transformation 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"

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)