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: "efficient nonnegative tucker decompositions algorithms and uniqueness"
  Abstract: Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data. However, as the data tensor often has multiple modes and is large scale, the existing NTD algorithms suffer from a very high computational complexity in terms of both storage and computation time, which has been one major obstacle for practical applications of NTD. To overcome these disadvantages, we show how low (multilinear) rank approximation (LRA) of tensors is able to significantly simplify the computation of the gradients of the cost function, upon which a family of efficient first-order NTD algorithms are developed. Besides dramatically reducing the storage complexity and running time, the new algorithms are quite flexible and robust to noise, because any well-established LRA approaches can be applied. We also show how nonnegativity incorporating sparsity substantially improves the uniqueness property and partially alleviates the curse of dimensionality of the Tucker decompositions. Simulation results on synthetic and real-world data justify the validity and high efficiency of the proposed NTD algorithms.

Connected through hyperedge HE-1 (induced by source paper "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"):
  with members:
    - "group component analysis for multiblock data common and individual feature extraction"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"

Connected through hyperedge HE-2 (induced by source paper "learning the hierarchical parts of objects by deep non smooth nonnegative matrix factorization"):
  with members:
    - "sparse deep nonnegative matrix factorization"
    - "a deep matrix factorization method for learning attribute representations"
    - "non negative matrix factorization with sparseness constraints"
    - "non negative sparse coding"

Connected through hyperedge HE-3 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-4 (induced by source paper "linked component analysis from matrices to high order tensors applications to biomedical data"):
  with members:
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "smooth parafac decomposition for tensor completion"
    - "robust principal component analysis on graphs"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "tensor networks for big data analytics and large scale optimization problems"
    - "independent vector analysis identification conditions and performance bounds"

Connected through hyperedge HE-5 (induced by source paper "tensor ring decomposition"):
  with members:
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "efficient tensor completion low rank tensor train"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "tensorizing neural networks"
    - "matrix product state for feature extraction of higher order tensors"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"

Connected through hyperedge HE-6 (induced by source paper "tensor networks for big data analytics and large scale optimization problems"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

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)