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: "nonparametric estimation of multi view latent variable models"
  Abstract: Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel method for learning multi-view latent variable models, allowing each mixture component to be nonparametric. The key idea of the method is to embed the joint distribution of a multi-view latent variable into a reproducing kernel Hilbert space, and then the latent parameters are recovered using a robust tensor power method. We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters. Thus, our non-parametric tensor approach to learning latent variable models enjoys good sample and computational efficiencies. Moreover, the non-parametric tensor power method compares favorably to EM algorithm and other existing spectral algorithms in our experiments.

Connected through hyperedge HE-1 (induced by source paper "spectral learning of binomial hmms for dna methylation data"):
  with members:
    - "spectral experts for estimating mixtures of linear regressions"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "long term forecasting using higher order tensor rnns"):
  with members:
    - "learning to reason with third order tensor products"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "generating long term trajectories using deep hierarchical networks"
    - "learning from multiway data simple and efficient tensor regression"
    - "higher order recurrent neural networks"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "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"

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"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
    - "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 "sequential local learning for latent graphical models"):
  with members:
    - "unsupervised learning of noisy or bayesian networks"
    - "spectral experts for estimating mixtures of linear regressions"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent junction trees"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-6 (induced by source paper "experimental results reinforcement learning of pomdps using spectral methods"):
  with members:
    - "reinforcement learning of pomdp s using spectral methods"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "efficient learning and planning with compressed predictive states"
    - "playing atari with deep reinforcement learning"
    - "a method of moments for mixture models and hidden markov models"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods"):
  with members:
    - "experimental results reinforcement learning of pomdps using spectral methods"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "a pac rl algorithm for episodic pomdps"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-8 (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"
    - "sample complexity analysis for learning overcomplete latent variable models through 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"
    - "what regularized auto encoders learn from the data generating distribution"

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)