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"
  Abstract: Spatial and spectral approaches are two major approaches for image processing tasks such as image classification and object recognition. Among many such algorithms, convolutional neural networks (CNNs) have recently achieved significant performance improvement in many challenging tasks. Since CNNs process images directly in the spatial domain, they are essentially spatial approaches. Given that spatial and spectral approaches are known to have different characteristics, it will be interesting to incorporate a spectral approach into CNNs. We propose a novel CNN architecture, wavelet CNNs, which combines a multiresolution analysis and CNNs into one model. Our insight is that a CNN can be viewed as a limited form of a multiresolution analysis. Based on this insight, we supplement missing parts of the multiresolution analysis via wavelet transform and integrate them as additional components in the entire architecture. Wavelet CNNs allow us to utilize spectral information which is mostly lost in conventional CNNs but useful in most image processing tasks. We evaluate the practical performance of wavelet CNNs on texture classification and image annotation. The experiments show that wavelet CNNs can achieve better accuracy in both tasks than existing models while having significantly fewer parameters than conventional CNNs.

Connected through hyperedge HE-1 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "forward stability of resnet and its variants"
    - "deep image prior"

Connected through hyperedge HE-2 (induced by source paper "waveletfcnn a deep time series classification model for wind turbine blade icing detection"):
  with members:
    - "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"
    - "are generative deep models for novelty detection truly better"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "fast and accurate time series classification with weasel"
    - "deep convolutional neural network for inverse problems in imaging"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-3 (induced by source paper "deep adaptive wavelet network"):
  with members:
    - "deep learning in the wavelet domain"
    - "scaling the scattering transform deep hybrid networks"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "improving interpretability of deep neural networks with semantic information"
    - "using filter banks in convolutional neural networks for texture classification"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "joint time frequency scattering"):
  with members:
    - "compressing the input for cnns with the first order scattering transform"
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "soundnet learning sound representations from unlabeled video"
    - "deep learning in neural networks"
    - "deep scattering spectrum"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning for multi scale changepoint detection in multivariate time series"):
  with members:
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "feature pyramid networks for object detection"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "hierarchical multiscale recurrent neural networks"
    - "end to end memory networks"
    - "m statistic for kernel change point detection"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "diagnosing convolutional neural networks using their spectral response"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "learning transferable architectures for scalable image recognition"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "analyzing stability of convolutional neural networks in the frequency domain"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)