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 "deep adaptive wavelet network"):
  Abstract: Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process. This paper aims at overcoming those limitations by proposing a deep neural network, which is designed in a systematic fashion and is interpretable, by integrating multiresolution analysis at the core of the deep neural network design. By using the lifting scheme, it is possible to generate a wavelet representation and design a network capable of learning wavelet coefficients in an end-to-end form. Compared to state-of-the-art architectures, the proposed model requires less hyper-parameter tuning and achieves competitive accuracy in image classification tasks

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 "wavelet convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "translating diffusion wavelets and regularisation into residual networks")
  Member "scaling the scattering transform deep hybrid networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analyzing the dependency of convnets on spatial information")
  Member "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "improving interpretability of deep neural networks with semantic information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explaining with counter visual attributes and examples")
  Member "using filter banks in convolutional neural networks for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local rotation invariance in 3d cnns")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "wide residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)