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: "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
  Abstract: Overcomplete latent representations have been very popular for unsupervised feature learning in recent years. In this paper, we specify which overcomplete models can be identified given observable moments of a certain order. We consider probabilistic admixture or topic models in the overcomplete regime, where the number of latent topics can greatly exceed the size of the observed word vocabulary. While general overcomplete topic models are not identifiable, we establish generic identifiability under a constraint, referred to as topic persistence. Our sufficient conditions for identifiability involve a novel set of "higher order" expansion conditions on the topic-word matrix or the population structure of the model. This set of higher-order expansion conditions allow for overcomplete models, and require the existence of a perfect matching from latent topics to higher order observed words. We establish that random structured topic models are identifiable w.h.p. in the overcomplete regime. Our identifiability results allows for general (non-degenerate) distributions for modeling the topic proportions, and thus, we can handle arbitrarily correlated topics in our framework. Our identifiability results imply uniqueness of a class of tensor decompositions with structured sparsity which is contained in the class of Tucker decompositions, but is more general than the Candecomp/Parafac (CP) decomposition.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "tensorizing neural networks"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"

Connected through hyperedge HE-3 (induced by source paper "minimal realization problems for jump linear systems"):
  with members:
    - "learning mixtures of gaussians in high dimensions"

Connected through hyperedge HE-4 (induced by source paper "sample complexity analysis for learning overcomplete latent variable models through tensor methods"):
  with members:
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "nonparametric estimation of multi view latent variable models"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-5 (induced by source paper "prior aware dual decomposition document specific topic inference for spectral topic models"):
  with members:
    - "provable algorithms for inference in topic models"
    - "spectral methods for correlated topic models"
    - "robust spectral inference for joint stochastic matrix factorization"
    - "probabilistic latent semantic analysis"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "on smoothing and inference for topic models"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-6 (induced by source paper "finding a sparse vector in a subspace linear sparsity using alternating directions"):
  with members:
    - "a geometric analysis of phase retrieval"
    - "simple efficient and neural algorithms for sparse coding"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "tensor vs matrix methods robust tensor decomposition under block sparse perturbations"
    - "sum of squares lower bounds for sparse pca"
    - "speeding up sum of squares for tensor decomposition and planted sparse vectors"
    - "finding a low rank basis in a matrix subspace"
    - "dual principal component pursuit"

Connected through hyperedge HE-7 (induced by source paper "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "fast detection of overlapping communities via online tensor methods"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "nonparametric estimation of multi view latent variable models"
    - "fourier pca and robust tensor decomposition"
    - "understanding alternating minimization for matrix completion"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-8 (induced by source paper "nonparametric estimation of multi view latent variable models"):
  with members:
    - "online tensor methods for learning latent variable models"
    - "identifying finite mixtures of nonparametric product distributions and causal inference of confounders"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for latent dirichlet allocation"
    - "a spectral algorithm for learning hidden markov 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)