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: "denoising and completion of 3d data via multidimensional dictionary learning"
  Abstract: In this paper a new dictionary learning algorithm for multidimensional data is proposed. Unlike most conventional dictionary learning methods which are derived for dealing with vectors or matrices, our algorithm, named KTSVD, learns a multidimensional dictionary directly via a novel algebraic approach for tensor factorization as proposed in [3, 12, 13]. Using this approach one can define a tensor-SVD and we propose to extend K-SVD algorithm used for 1-D data to a K-TSVD algorithm for handling 2-D and 3-D data. Our algorithm, based on the idea of sparse coding (using group-sparsity over multidimensional coefficient vectors), alternates between estimating a compact representation and dictionary learning. We analyze our KTSVD algorithm and demonstrate its result on video completion and multispectral image denoising.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "tensorizing neural networks"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"

Connected through hyperedge HE-2 (induced by source paper "vest very sparse tucker factorization of large scale tensors"):
  with members:
    - "ctd fast accurate and interpretable method for static and dynamic tensor decompositions"
    - "scalable tucker factorization for sparse tensors algorithms and discoveries"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "minimax lower bounds on dictionary learning for tensor data"):
  with members:
    - "minimax lower bounds for kronecker structured dictionary learning"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"

Connected through hyperedge HE-4 (induced by source paper "minimax lower bounds for kronecker structured dictionary learning"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

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.LG(Machine Learning)