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: "redundant wavelets on graphs and high dimensional data clouds"
  Abstract: In this paper, we propose a new redundant wavelet transform applicable to scalar functions defined on high dimensional coordinates, weighted graphs and networks. The proposed transform utilizes the distances between the given data points to construct tree-like structures. We modify the filter-bank decomposition scheme of the redundant wavelet transform by adding in each decomposition level operators that reorder the approximation coefficients. These reordering operators are derived by organizing the tree-node features so as to shorten the path that passes through these points. We explore the use of the proposed transform for the recovery of labels defined on point clouds and to image denoising, and show that in both cases the results are promising.

Connected through hyperedge HE-1 (induced by source paper "finding gems multi scale dictionaries for high dimensional graph signals"):
  with members:
    - "structure aware classification using supervised dictionary learning"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "learning laplacian matrix in smooth graph signal representations"
    - "learning parametric dictionaries for signals on graphs"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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"
    - "poisson noise reduction with non local pca"
    - "generalized tree based wavelet transform"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-3 (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"
    - "chebyshev polynomial approximation for distributed signal processing"
    - "generalized tree based wavelet transform"

Connected through hyperedge HE-4 (induced by source paper "image processing using smooth ordering of its patches"):
  with members:
    - "generalized tree based wavelet transform"
    - "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)