human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi path learnable wavelet neural network for image classification"):
  Abstract: Despite the remarkable success of deep learning in pattern recognition, deep network models face the problem of training a large number of parameters. In this paper, we propose and evaluate a novel multi-path wavelet neural network architecture for image classification with far less number of trainable parameters. The model architecture consists of a multi-path layout with several levels of wavelet decompositions performed in parallel followed by fully connected layers. These decomposition operations comprise wavelet neurons with learnable parameters, which are updated during the training phase using the back-propagation algorithm. We evaluate the performance of the introduced network using common image datasets without data augmentation except for SVHN and compare the results with influential deep learning models. Our findings support the possibility of reducing the number of parameters significantly in deep neural networks without compromising its accuracy.

Includes members:
  Member "deep learning in the wavelet domain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a learnable scatternet locally invariant convolutional layers")
  Member "detailed dense inference with convolutional neural networks via discrete wavelet transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning in the wavelet domain")
  Member "wavelet based edge feature enhancement for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harmonic convolutional networks based on discrete cosine transform")
  Member "multi level wavelet cnn for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning based video coding a review and a case study")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "scaling the scattering transform deep hybrid networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analyzing the dependency of convnets on spatial information")
  Member "scatternet hybrid deep learning shdl network for object classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised learning using pretrained cnn and associative memory bank")
  Member "wavelet convolutional neural networks for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "harmonic convolutional networks based on discrete cosine transform")

Question: Please tell me which class the source paper 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)