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: "wavelet convolutional neural networks for texture classification"
  Abstract: Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a difficult problem since textures usually do not contain enough information regarding the shape of object. In image processing, texture classification has been traditionally studied well with spectral analyses which exploit repeated structures in many textures. Since CNNs process images as-is in the spatial domain whereas spectral analyses process images in the frequency domain, these models have different characteristics in terms of performance. We propose a novel CNN architecture, wavelet CNNs, which integrates a spectral analysis into CNNs. Our insight is that the pooling layer and the convolution layer can be viewed as a limited form of a spectral analysis. Based on this insight, we generalize both layers to perform a spectral analysis with wavelet transform. Wavelet CNNs allow us to utilize spectral information which is lost in conventional CNNs but useful in texture classification. The experiments demonstrate that our model achieves better accuracy in texture classification than existing models. We also show that our model has significantly fewer parameters than CNNs, making our model easier to train with less memory.

Connected through hyperedge HE-1 (induced by source paper "harmonic convolutional networks based on discrete cosine transform"):
  with members:
    - "basisconv a method for compressed representation and learning in cnns"
    - "scattering networks for hybrid representation learning"
    - "hybrid task cascade for instance segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "progressive neural architecture search"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "specnet spectral domain convolutional neural network"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "sort second order response transform for visual recognition"
    - "memory efficient implementation of densenets"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-4 (induced by source paper "fully hyperbolic convolutional neural networks"):
  with members:
    - "fast video object segmentation with spatio temporal gans"
    - "scale recurrent network for deep image deblurring"
    - "deep neural networks motivated by partial differential equations"
    - "switchable temporal propagation network"
    - "multi level wavelet cnn for image restoration"
    - "the reversible residual network backpropagation without storing activations"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "an end to end 3d convolutional neural network for action detection and segmentation in videos"

Connected through hyperedge HE-5 (induced by source paper "photorealistic style transfer via wavelet transforms"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer with deep feature reshuffle"
    - "nsml meet the mlaas platform with a real world case study"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "scaling the scattering transform deep hybrid networks"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-6 (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"
    - "a multi branch convolutional neural network for detecting double jpeg compression"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-7 (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"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "faster and accurate classification for jpeg2000 compressed images in networked applications"):
  with members:
    - "towards image understanding from deep compression without decoding"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "an end to end compression framework based on convolutional neural networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

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)