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 "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"):
  Abstract: Author(s): Anandkumar, Animashree; Ge, Rong; Janzamin, Majid | Abstract: In this paper, we provide local and global convergence guarantees for recovering CP (Candecomp/Parafac) tensor decomposition. The main step of the proposed algorithm is a simple alternating rank-$1$ update which is the alternating version of the tensor power iteration adapted for asymmetric tensors. Local convergence guarantees are established for third order tensors of rank $k$ in $d$ dimensions, when $k=o \bigl( d^{1.5} \bigr)$ and the tensor components are incoherent. Thus, we can recover overcomplete tensor decomposition. We also strengthen the results to global convergence guarantees under stricter rank condition $k \le \beta d$ (for arbitrary constant $\beta g 1$) through a simple initialization procedure where the algorithm is initialized by top singular vectors of random tensor slices. Furthermore, the approximate local convergence guarantees for $p$-th order tensors are also provided under rank condition $k=o \bigl( d^{p/2} \bigr)$. The guarantees also include tight perturbation analysis given noisy tensor.

Includes members:
  Member "sample complexity analysis for learning overcomplete latent variable models through tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods")
  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 "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "smoothed analysis of tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improper learning for non stochastic control")
  Member "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "nonparametric estimation of multi view latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral learning of binomial hmms for dna methylation data")
  Member "fourier pca and robust tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning two layer rectified neural networks in polynomial time")
  Member "understanding alternating minimization for matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling")

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)