human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "simultaneous diagonalization the asymmetric low rank and noisy settings":
    also connected through other hyperedges:
      (none)
  Member "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "fast detection of overlapping communities via online tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guaranteed scalable learning of latent tree models")
  Member "unsupervised learning of noisy or bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable meta learning of linear representations")
  Member "symmetric tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse interpolation in terms of multivariate chebyshev polynomials")
  Member "most tensor problems are np hard":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable tensor ring completion")

Question: Please tell me which class the source paper 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)