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: "efficient learning of simplices"
  Abstract: We show an efficient algorithm for the following problem: Given uniformly random points from an arbitrary n-dimensional simplex, estimate the simplex. The size of the sample and the number of arithmetic operations of our algorithm are polynomial in n. This answers a question of Frieze, Jerrum and Kannan [FJK]. Our result can also be interpreted as efficiently learning the intersection of n+1 half-spaces in R^n in the model where the intersection is bounded and we are given polynomially many uniform samples from it. Our proof uses the local search technique from Independent Component Analysis (ICA), also used by [FJK]. Unlike these previous algorithms, which were based on analyzing the fourth moment, ours is based on the third moment. #R##N#We also show a direct connection between the problem of learning a simplex and ICA: a simple randomized reduction to ICA from the problem of learning a simplex. The connection is based on a known representation of the uniform measure on a simplex. Similar representations lead to a reduction from the problem of learning an affine transformation of an n-dimensional l_p ball to ICA.

Connected through hyperedge HE-1 (induced by source paper "finding a latent k simplex in o k nnz data time via subset smoothing"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "a tensor approach to learning mixed membership community models"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "improved spectral norm bounds for clustering"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-2 (induced by source paper "learning from satisfying assignments under continuous distributions"):
  with members:
    - "efficient convex optimization with membership oracles"
    - "a fully polynomial time approximation scheme for approximating a sum of random variables"

Connected through hyperedge HE-3 (induced by source paper "on statistical learning of simplices unmixing problem revisited"):
  with members:
    - "spectral unmixing via data guided sparsity"

Connected through hyperedge HE-4 (induced by source paper "geometric methods for robust data analysis in high dimension"):
  with members:
    - "heavy tailed independent component analysis"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"
    - "a two round variant of em for gaussian mixtures"
    - "blind signal separation in the presence of gaussian noise"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"

Connected through hyperedge HE-5 (induced by source paper "the hidden convexity of spectral clustering"):
  with members:
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "fourier pca and robust tensor decomposition"):
  with members:
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "blind signal separation in the presence of gaussian noise"
    - "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"

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)