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: "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"
  Abstract: Many idealized problems in signal processing, machine learning, and statistics can be reduced to the problem of finding the symmetric canonical decomposition of an underlying symmetric and orthogonally decomposable (SOD) tensor. Drawing inspiration from the matrix case, the successive rank-one approximation (SROA) scheme has been proposed and shown to yield this tensor decomposition exactly, and a plethora of numerical methods have thus been developed for the tensor rank-one approximation problem. In practice, however, the inevitable errors (say) from estimation, computation, and modeling necessitate that the input tensor can only be assumed to be a nearly SOD tensor---i.e., a symmetric tensor slightly perturbed from the underlying SOD tensor. This paper shows that even in the presence of perturbation, SROA can still robustly recover the symmetric canonical decomposition of the underlying tensor. It is shown that when the perturbation error is small enough, the approximation errors do not accumulate with ...

Connected through hyperedge HE-1 (induced by source paper "revisiting skip gram negative sampling model with regularization"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding composition of word embeddings via tensor decomposition"
    - "riemannian optimization for skip gram negative sampling"
    - "advances in pre training distributed word representations"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "greedy approaches to symmetric orthogonal tensor decomposition"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "greedy approaches to symmetric orthogonal tensor decomposition"):
  with members:
    - "tensor decompositions via two mode higher order svd hosvd"
    - "complete dictionary recovery over the sphere"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-3 (induced by source paper "tensor decompositions via two mode higher order svd hosvd"):
  with members:
    - "tensor factorization via matrix factorization"
    - "max vs min tensor decomposition and ica with nearly linear sample complexity"
    - "a statistical model for tensor pca"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "most tensor problems are np hard"

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)