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 based edge feature enhancement for convolutional neural networks"
  Abstract: Convolutional neural networks are able to perform a hierarchical learning process starting with local features. However, a limited attention is paid to enhancing such elementary level features like edges. We propose and evaluate two wavelet-based edge feature enhancement methods to preprocess the input images to convolutional neural networks. The first method develops feature enhanced representations by decomposing the input images using wavelet transform and limited reconstructing subsequently. The second method develops such feature enhanced inputs to the network using local modulus maxima of wavelet coefficients. For each method, we have developed a new preprocessing layer by implementing each purposed method and have appended to the network architecture. Our empirical evaluations demonstrate that the proposed methods are outperforming the baselines and previously published work with significant accuracy gains.

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"
    - "progressive neural architecture search"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "harmonic networks with limited training samples"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "gabor convolutional 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"
    - "wavelet convolutional neural networks for texture classification"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-3 (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"
    - "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"
    - "wavelet convolutional neural networks for texture classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "harmonic networks integrating spectral information into cnns"):
  with members:
    - "towards image understanding from deep compression without decoding"
    - "scaling the scattering transform deep hybrid networks"
    - "wavelet convolutional neural networks for texture classification"
    - "harmonic networks deep translation and rotation equivariance"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"
    - "deep clustered convolutional kernels"

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)