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: "generalized tree based wavelet transform"
  Abstract: In this paper we propose a new wavelet transform applicable to functions defined on high dimensional data, weighted graphs and networks. The proposed method generalizes the Haar-like transform recently introduced by Gavish , and can also construct data adaptive orthonormal wavelets beyond Haar. It is defined via a hierarchical tree, which is assumed to capture the geometry and structure of the input data, and is applied to the data using a modified version of the common one-dimensional (1D) wavelet filtering and decimation scheme. The adaptivity of this wavelet scheme is obtained by permutations derived from the tree and applied to the approximation coefficients in each decomposition level, before they are filtered. We show that the proposed transform is more efficient than both the 1D and two-dimension 2D separable wavelet transforms in representing images. We also explore the application of the proposed transform to image denoising, and show that combined with a subimage averaging scheme, it achieves denoising results which are similar to those obtained with the K-SVD algorithm.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural networks on graphs with fast localized spectral filtering"):
  with members:
    - "toward an uncertainty principle for weighted graphs"
    - "deep convolutional networks on graph structured data"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "spatial transformer networks"
    - "on the degrees of freedom of signals on graphs"
    - "adam a method for stochastic optimization"
    - "unsupervised deep haar scattering on graphs"
    - "a multiscale pyramid transform for graph signals"

Connected through hyperedge HE-2 (induced by source paper "patch ordering as a regularization for inverse problems in image processing"):
  with members:
    - "boosting of image denoising algorithms"
    - "sparsity based poisson denoising with dictionary learning"
    - "image processing using smooth ordering of its patches"
    - "redundant wavelets on graphs and high dimensional data clouds"
    - "poisson noise reduction with non local pca"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-3 (induced by source paper "learning laplacian matrix in smooth graph signal representations"):
  with members:
    - "learning parametric dictionaries for signals on graphs"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "discrete signal processing on graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"
    - "representation learning a review and new perspectives"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-4 (induced by source paper "learning parametric dictionaries for signals on graphs"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"
    - "redundant wavelets on graphs and high dimensional data clouds"
    - "chebyshev polynomial approximation for distributed signal processing"

Connected through hyperedge HE-5 (induced by source paper "image processing using smooth ordering of its patches"):
  with members:
    - "redundant wavelets on graphs and high dimensional data clouds"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

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)