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: "smoothed analysis of tensor decompositions"
  Abstract: Low rank tensor decompositions are a powerful tool for learning generative models, and uniqueness results give them a significant advantage over matrix decomposition methods. However, tensors pose significant algorithmic challenges and tensors analogs of much of the matrix algebra toolkit are unlikely to exist because of hardness results. Efficient decomposition in the overcomplete case (where rank exceeds dimension) is particularly challenging. We introduce a smoothed analysis model for studying these questions and develop an efficient algorithm for tensor decomposition in the highly overcomplete case (rank polynomial in the dimension). In this setting, we show that our algorithm is robust to inverse polynomial error -- a crucial property for applications in learning since we are only allowed a polynomial number of samples. While algorithms are known for exact tensor decomposition in some overcomplete settings, our main contribution is in analyzing their stability in the framework of smoothed analysis. #R##N#Our main technical contribution is to show that tensor products of perturbed vectors are linearly independent in a robust sense (i.e. the associated matrix has singular values that are at least an inverse polynomial). This key result paves the way for applying tensor methods to learning problems in the smoothed setting. In particular, we use it to obtain results for learning multi-view models and mixtures of axis-aligned Gaussians where there are many more "components" than dimensions. The assumption here is that the model is not adversarially chosen, formalized by a perturbation of model parameters. We believe this an appealing way to analyze realistic instances of learning problems, since this framework allows us to overcome many of the usual limitations of using tensor methods.

Connected through hyperedge HE-1 (induced by source paper "improper learning for non stochastic control"):
  with members:
    - "naive exploration is optimal for online lqr"
    - "learning linear dynamical systems with semi parametric least squares"
    - "introduction to online convex optimization"
    - "finite sample analysis of stochastic system identification"
    - "online control with adversarial disturbances"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "logarithmic regret for online control"

Connected through hyperedge HE-2 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians"):
  with members:
    - "em converges for a mixture of many linear regressions"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "sample efficient learning of mixtures"
    - "mixture models robustness and sum of squares proofs"
    - "on learning mixtures of well separated gaussians"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning mixtures of gaussians in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "private hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

Connected through hyperedge HE-4 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians"):
  with members:
    - "private hypothesis selection"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "differentially private k means with constant multiplicative error"
    - "inspectre privately estimating the unseen"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "finite sample differentially private confidence intervals"
    - "clustering algorithms for the centralized and local models"

Connected through hyperedge HE-5 (induced by source paper "stability of linear structural equation models of causal inference"):
  with members:
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "graphical condition for identification in recursive sem"

Connected through hyperedge HE-6 (induced by source paper "learning two layer neural networks with symmetric inputs"):
  with members:
    - "end to end learning of a convolutional neural network via deep tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "learning one convolutional layer with overlapping patches"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

Connected through hyperedge HE-7 (induced by source paper "smoothed analysis in unsupervised learning via decoupling"):
  with members:
    - "smoothed analysis of discrete tensor decomposition and assemblies of neurons"
    - "learning overcomplete hmms"
    - "local max cut in smoothed polynomial time"
    - "polynomial time tensor decompositions with sum of squares"
    - "learning mixtures of gaussians in high dimensions"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "minimal realization problems for hidden markov models"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-8 (induced by source paper "on the restricted isometry of the columnwise khatri rao product"):
  with members:
    - "graph sampling for covariance estimation"
    - "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "hidden cliques and the certification of the restricted isometry property"
    - "new bounds for restricted isometry constants"

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.DS(Data Structures and Algorithms)