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: "learning mixtures of arbitrary distributions over large discrete domains"
  Abstract: We give an algorithm for learning a mixture of {\em unstructured} distributions. This problem arises in various unsupervised learning scenarios, for example in learning {\em topic models} from a corpus of documents spanning several topics. We show how to learn the constituents of a mixture of $k$ arbitrary distributions over a large discrete domain $[n]=\{1,2,\dots,n\}$ and the mixture weights, using $O(n\polylog n)$ samples. (In the topic-model learning setting, the mixture constituents correspond to the topic distributions.) This task is information-theoretically impossible for $k>1$ under the usual sampling process from a mixture distribution. However, there are situations (such as the above-mentioned topic model case) in which each sample point consists of several observations from the same mixture constituent. This number of observations, which we call the {\em "sampling aperture"}, is a crucial parameter of the problem. We obtain the {\em first} bounds for this mixture-learning problem {\em without imposing any assumptions on the mixture constituents.} We show that efficient learning is possible exactly at the information-theoretically least-possible aperture of $2k-1$. Thus, we achieve near-optimal dependence on $n$ and optimal aperture. While the sample-size required by our algorithm depends exponentially on $k$, we prove that such a dependence is {\em unavoidable} when one considers general mixtures. A sequence of tools contribute to the algorithm, such as concentration results for random matrices, dimension reduction, moment estimations, and sensitivity analysis.

Connected through hyperedge HE-1 (induced by source paper "learning sums of independent random variables with sparse collective support"):
  with members:
    - "boolean function analysis meets stochastic optimization an approximation scheme for stochastic knapsack"
    - "playing anonymous games using simple strategies"
    - "a size free clt for poisson multinomials and its applications"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "big data on the rise testing monotonicity of distributions"
    - "learning arbitrary statistical mixtures of discrete distributions"

Connected through hyperedge HE-2 (induced by source paper "state aggregation learning from markov transition data"):
  with members:
    - "sample optimal parametric q learning with linear transition models"
    - "on learning markov chains"
    - "scalable bilinear pi learning using state and action features"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "on learning parametric output hmms"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-3 (induced by source paper "learning arbitrary statistical mixtures of discrete distributions"):
  with members:
    - "probabilistic latent semantic analysis"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "learning k modal distributions via testing"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "smoothed analysis of tensor decompositions"):
  with members:
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "fourier pca and robust tensor decomposition"
    - "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"
    - "a spectral algorithm for latent dirichlet allocation"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"

Connected through hyperedge HE-5 (induced by source paper "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"):
  with members:
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "on model based rip 1 matrices"
    - "fourier pca and robust tensor decomposition"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "representation learning a review and new perspectives"
    - "sparse coding for multitask and transfer learning"
    - "tensor decompositions for learning latent variable models"
    - "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)