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: "deep learning in the wavelet domain"
  Abstract: This paper examines the possibility of, and the possible advantages to learning the filters of convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are stimulated by both Mallat's scattering transform and the idea of filtering in the Fourier domain. It is important to explore new spaces in which to learn, as these may provide inherent advantages that are not available in the pixel space. However, the scattering transform is limited by its inability to learn in between scattering orders, and any Fourier domain filtering is limited by the large number of filter parameters needed to get localized filters. Instead we consider filtering in the wavelet domain with learnable filters. The wavelet space allows us to have local, smooth filters with far fewer parameters, and learnability can give us flexibility. We present a novel layer which takes CNN activations into the wavelet space, learns parameters and returns to the pixel space. This allows it to be easily dropped in to any neural network without affecting the structure. As part of this work, we show how to pass gradients through a multirate system and give preliminary results.

Connected through hyperedge HE-1 (induced by source paper "a learnable scatternet locally invariant convolutional layers"):
  with members:
    - "scaling the scattering transform deep hybrid networks"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "visualizing and improving scattering networks"
    - "dynamic steerable blocks in deep residual networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "multi path learnable wavelet neural network for image classification"):
  with members:
    - "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"
    - "wavelet convolutional neural networks for texture classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep adaptive wavelet network"):
  with members:
    - "wavelet convolutional neural networks"
    - "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"

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)