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: "tensor factorization via matrix factorization"
  Abstract: Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for tensor factorization have not reached the level of maturity of matrix factorization methods. In this paper, we propose a new method for CP tensor factorization that uses random projections to reduce the problem to simultaneous matrix diagonalization. Our method is conceptually simple and also applies to non-orthogonal and asymmetric tensors of arbitrary order. We prove that a small number random projections essentially preserves the spectral information in the tensor, allowing us to remove the dependence on the eigengap that plagued earlier tensor-to-matrix reductions. Experimentally, our method outperforms existing tensor factorization methods on both simulated data and two real datasets.

Connected through hyperedge HE-1 (induced by source paper "embedding syntax and semantics of prepositions via tensor decomposition"):
  with members:
    - "ontology aware token embeddings for prepositional phrase attachment"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "embedding lexical features via low rank tensors"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-2 (induced by source paper "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "learning from multiway data simple and efficient tensor regression"
    - "polynomial time tensor decompositions with sum of squares"
    - "reinforcement learning of pomdps using spectral methods"
    - "tensorly tensor learning in python"
    - "learning mixtures of gaussians in high dimensions"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"

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 via two mode higher order svd hosvd"):
  with members:
    - "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unsupervised risk estimation using only conditional independence structure"):
  with members:
    - "spectral experts for estimating mixtures of linear regressions"
    - "a method of moments for mixture models and hidden markov models"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-6 (induced by source paper "approximate joint matrix triangularization"):
  with members:
    - "tensor decompositions for learning latent variable models"

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)