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: "uniqueness of tensor decompositions with applications to polynomial identifiability"
  Abstract: We give a robust version of the celebrated result of Kruskal on the uniqueness of tensor decompositions: we prove that given a tensor whose decomposition satisfies a robust form of Kruskal's rank condition, it is possible to approximately recover the decomposition if the tensor is known up to a sufficiently small (inverse polynomial) error. #R##N#Kruskal's theorem has found many applications in proving the identifiability of parameters for various latent variable models and mixture models such as Hidden Markov models, topic models etc. Our robust version immediately implies identifiability using only polynomially many samples in many of these settings. This polynomial identifiability is an essential first step towards efficient learning algorithms for these models. #R##N#Recently, algorithms based on tensor decompositions have been used to estimate the parameters of various hidden variable models efficiently in special cases as long as they satisfy certain "non-degeneracy" properties. Our methods give a way to go beyond this non-degeneracy barrier, and establish polynomial identifiability of the parameters under much milder conditions. Given the importance of Kruskal's theorem in the tensor literature, we expect that this robust version will have several applications beyond the settings we explore in this work.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on learning mixtures of well separated gaussians"):
  with members:
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "learning mixtures of gaussians in high dimensions"
    - "tight bounds for learning a mixture of two gaussians"
    - "smoothed analysis of tensor decompositions"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-3 (induced by source paper "learning overcomplete hmms"):
  with members:
    - "prediction with a short memory"
    - "learning parametric output hmms with two aliased states"
    - "minimal realization problems for hidden markov models"
    - "smoothed analysis of tensor decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "agnostic estimation of mean and covariance"):
  with members:
    - "robust estimators in high dimensions without the computational intractability"
    - "max vs min tensor decomposition and ica with nearly linear sample complexity"
    - "fourier pca and robust tensor decomposition"
    - "blind signal separation in the presence of gaussian noise"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "tensor decompositions via two mode higher order svd hosvd"):
  with members:
    - "tensor factorization via matrix factorization"
    - "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"
    - "max vs min tensor decomposition and ica with nearly linear sample complexity"
    - "a statistical model for tensor pca"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "most tensor problems are np hard"

Connected through hyperedge HE-6 (induced by source paper "dictionary learning and tensor decomposition via the sum of squares method"):
  with members:
    - "more algorithms for provable dictionary learning"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "fourier pca and robust tensor decomposition"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-7 (induced by source paper "learning mixtures of ranking models"):
  with members:
    - "smoothed analysis of tensor decompositions"
    - "fourier pca and robust tensor decomposition"
    - "dirichlet process mixtures of generalized mallows models"
    - "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"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "sorting from noisy information"

Connected through hyperedge HE-8 (induced by source paper "minimal realization problems for hidden markov models"):
  with members:
    - "smoothed analysis of tensor decompositions"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for learning hidden markov models"
    - "learning nonsingular phylogenies and 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.DS(Data Structures and Algorithms)