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: "low complexity damped gauss newton algorithms for candecomp parafac"
  Abstract: The damped Gauss-Newton (dGN) algorithm for CANDECOMP/PARAFAC (CP) decomposition can handle the challenges of collinearity of factors and different magnitudes of factors; nevertheless, for factorization of an $N$-D tensor of size $I_1\times I_N$ with rank $R$, the algorithm is computationally demanding due to construction of large approximate Hessian of size $(RT \times RT)$ and its inversion where $T = \sum_n I_n$. In this paper, we propose a fast implementation of the dGN algorithm which is based on novel expressions of the inverse approximate Hessian in block form. The new implementation has lower computational complexity, besides computation of the gradient (this part is common to both methods), requiring the inversion of a matrix of size $NR^2\times NR^2$, which is much smaller than the whole approximate Hessian, if $T \gg NR$. In addition, the implementation has lower memory requirements, because neither the Hessian nor its inverse never need to be stored in their entirety. A variant of the algorithm working with complex valued data is proposed as well. Complexity and performance of the proposed algorithm is compared with those of dGN and ALS with line search on examples of difficult benchmark tensors.

Connected through hyperedge HE-1 (induced by source paper "tensor networks for latent variable analysis higher order canonical polyadic decomposition"):
  with members:
    - "vectorial dimension reduction for tensors based on bayesian inference"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "speeding up convolutional neural networks with low rank expansions"
    - "smoothed analysis of tensor decompositions"

Connected through hyperedge HE-2 (induced by source paper "error preserving correction for cpd and bounded norm cpd"):
  with members:
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "nonnegative approximations of nonnegative tensors"

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 "tensor decompositions for signal processing applications from two way to multiway component analysis"):
  with members:
    - "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"
    - "dintucker scaling up gaussian process models on multidimensional arrays with billions of elements"
    - "nonnegative approximations of nonnegative tensors"

Connected through hyperedge HE-5 (induced by source paper "era of big data processing a new approach via tensor networks and tensor decompositions"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "overview of constrained parafac models"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
    - "higher order partial least squares hopls a generalized multilinear regression method"

Connected through hyperedge HE-6 (induced by source paper "tensor networks for big data analytics and large scale optimization problems"):
  with members:
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "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"

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.NA(Numerical Analysis)