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: "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
  Abstract: Author(s): Anandkumar, Animashree; Ge, Rong; Janzamin, Majid | Abstract: We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space can exceed the observed dimensionality. In particular, we consider multiview mixtures, spherical Gaussian mixtures, ICA, and sparse coding models. We provide tight concentration bounds for empirical moments through novel covering arguments. We analyze parameter recovery through a simple tensor power update algorithm. In the semi-supervised setting, we exploit the label or prior information to get a rough estimate of the model parameters, and then refine it using the tensor method on unlabeled samples. We establish that learning is possible when the number of components scales as $k=o(d^{p/2})$, where $d$ is the observed dimension, and $p$ is the order of the observed moment employed in the tensor method. Our concentration bound analysis also leads to minimax sample complexity for semi-supervised learning of spherical Gaussian mixtures. In the unsupervised setting, we use a simple initialization algorithm based on SVD of the tensor slices, and provide guarantees under the stricter condition that $k\le \beta d$ (where constant $\beta$ can be larger than $1$), where the tensor method recovers the components under a polynomial running time (and exponential in $\beta$). Our analysis establishes that a wide range of overcomplete latent variable models can be learned efficiently with low computational and sample complexity through tensor decomposition methods.

Connected through hyperedge HE-1 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods"):
  with members:
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "global optimality in tensor factorization deep learning and beyond"
    - "train faster generalize better stability of stochastic gradient descent"
    - "on the computational efficiency of training neural networks"
    - "provable methods for training neural networks with sparse connectivity"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "almost optimal pseudorandom generators for spherical caps"

Connected through hyperedge HE-2 (induced by source paper "training input output recurrent neural networks through spectral methods"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "reinforcement learning of pomdp s using spectral methods"
    - "strongly typed recurrent neural networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "score function features for discriminative learning matrix and tensor framework"
    - "nonparametric estimation of multi view latent variable models"
    - "what regularized auto encoders learn from the data generating distribution"

Connected through hyperedge HE-3 (induced by source paper "score function features for discriminative learning matrix and tensor framework"):
  with members:
    - "how transferable are features in deep neural networks"
    - "provable tensor methods for learning mixtures of classifiers"
    - "provable methods for training neural networks with sparse connectivity"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "provable bounds for learning some deep representations"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "new algorithms for learning incoherent and overcomplete dictionaries"

Connected through hyperedge HE-4 (induced by source paper "provable tensor methods for learning mixtures of generalized linear models"):
  with members:
    - "learning mixtures of ranking models"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "score function features for discriminative learning matrix and tensor framework"
    - "fast detection of overlapping communities via online tensor methods"
    - "discriminative features via generalized eigenvectors"
    - "learning mixtures of linear classifiers"
    - "nonparametric estimation of multi view latent variable models"
    - "online tensor methods for learning latent variable models"

Connected through hyperedge HE-5 (induced by source paper "analyzing tensor power method dynamics in overcomplete regime"):
  with members:
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "fast detection of overlapping communities via online tensor methods"
    - "smoothed analysis of tensor decompositions"
    - "fourier pca and robust tensor decomposition"
    - "representation learning a review and new perspectives"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"

Connected through hyperedge HE-6 (induced by source paper "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"):
  with members:
    - "fast detection of overlapping communities via online tensor methods"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
    - "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-7 (induced by source paper "score function features for discriminative learning"):
  with members:
    - "provable methods for training neural networks with sparse connectivity"
    - "provable tensor methods for learning mixtures of generalized linear models"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "discriminative features via generalized eigenvectors"
    - "online tensor methods for learning latent variable models"
    - "representation learning a review and new perspectives"

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)