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: "fourier pca and robust tensor decomposition"
  Abstract: Fourier PCA is Principal Component Analysis of a matrix obtained from higher order derivatives of the logarithm of the Fourier transform of a distribution.We make this method algorithmic by developing a tensor decomposition method for a pair of tensors sharing the same vectors in rank-$1$ decompositions. Our main application is the first provably polynomial-time algorithm for underdetermined ICA, i.e., learning an $n \times m$ matrix $A$ from observations $y=Ax$ where $x$ is drawn from an unknown product distribution with arbitrary non-Gaussian components. The number of component distributions $m$ can be arbitrarily higher than the dimension $n$ and the columns of $A$ only need to satisfy a natural and efficiently verifiable nondegeneracy condition. As a second application, we give an alternative algorithm for learning mixtures of spherical Gaussians with linearly independent means. These results also hold in the presence of Gaussian noise.

Connected through hyperedge HE-1 (induced by source paper "learning two layer rectified neural networks in polynomial time"):
  with members:
    - "complexity of training relu neural networks"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one convolutional layer with overlapping patches"
    - "the computational complexity of training relu s"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"

Connected through hyperedge HE-2 (induced by source paper "towards learning sparsely used dictionaries with arbitrary supports"):
  with members:
    - "outlier robust moment estimation via sum of squares"
    - "clustering semi random mixtures of gaussians"
    - "an improved analysis of the er spud dictionary learning algorithm"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "on the effect of randomness on planted 3 coloring models"
    - "simple efficient and neural algorithms for sparse coding"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"

Connected through hyperedge HE-3 (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-4 (induced by source paper "list decodable robust mean estimation and learning mixtures of spherical gaussians"):
  with members:
    - "certified defenses for data poisoning attacks"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "on learning mixtures of well separated gaussians"
    - "learning from untrusted data"

Connected through hyperedge HE-5 (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"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "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"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-6 (induced by source paper "drop dimensionality reduction optimization for time series"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "macrobase prioritizing attention in fast data"
    - "fma a dataset for music analysis"
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "training region based object detectors with online hard example mining"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "spectrum estimation from samples"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"

Connected through hyperedge HE-7 (induced by source paper "on the optimization landscape of tensor decompositions"):
  with members:
    - "polynomial time tensor decompositions with sum of squares"
    - "matrix completion has no spurious local minimum"
    - "gradient descent learns linear dynamical systems"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "the loss surfaces of multilayer networks"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-8 (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"
    - "nonparametric estimation of multi view latent variable models"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

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)