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: "near optimal sample estimators for spherical gaussian mixtures"
  Abstract: Statistical and machine-learning algorithms are frequently applied to high-dimensional data. In many of these applications data is scarce, and often much more costly than computation time. We provide the first sample-efficient polynomial-time estimator for high-dimensional spherical Gaussian mixtures. #R##N#For mixtures of any $k$ $d$-dimensional spherical Gaussians, we derive an intuitive spectral-estimator that uses $\mathcal{O}_k\bigl(\frac{d\log^2d}{\epsilon^4}\bigr)$ samples and runs in time $\mathcal{O}_{k,\epsilon}(d^3\log^5 d)$, both significantly lower than previously known. The constant factor $\mathcal{O}_k$ is polynomial for sample complexity and is exponential for the time complexity, again much smaller than what was previously known. We also show that $\Omega_k\bigl(\frac{d}{\epsilon^2}\bigr)$ samples are needed for any algorithm. Hence the sample complexity is near-optimal in the number of dimensions. #R##N#We also derive a simple estimator for one-dimensional mixtures that uses $\mathcal{O}\bigl(\frac{k \log \frac{k}{\epsilon} }{\epsilon^2} \bigr)$ samples and runs in time $\widetilde{\mathcal{O}}\left(\bigl(\frac{k}{\epsilon}\bigr)^{3k+1}\right)$. Our other technical contributions include a faster algorithm for choosing a density estimate from a set of distributions, that minimizes the $\ell_1$ distance to an unknown underlying distribution.

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 "density estimation for shift invariant multidimensional distributions"):
  with members:
    - "learning sums of independent random variables with sparse collective support"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-3 (induced by source paper "mixture models robustness and sum of squares proofs"):
  with members:
    - "thresholding based efficient outlier robust pca"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"

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 "near optimal sample complexity bounds for robust learning of gaussians mixtures via compression schemes"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "sample efficient learning of mixtures"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning multivariate log concave distributions"
    - "sample compression schemes for vc classes"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-6 (induced by source paper "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robust sparse estimation tasks in high dimensions"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "a size free clt for poisson multinomials and its applications"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "a general characterization of the statistical query complexity"
    - "beyond spectral tight bounds for planted gaussians"

Connected through hyperedge HE-7 (induced by source paper "robust estimators in high dimensions without the computational intractability"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robust sparse estimation tasks in high dimensions"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "a size free clt for poisson multinomials and its applications"

Connected through hyperedge HE-8 (induced by source paper "efficient robust proper learning of log concave distributions"):
  with members:
    - "a size free clt for poisson multinomials and its applications"
    - "learning multivariate log concave distributions"
    - "a nearly optimal and agnostic algorithm for properly learning a mixture of k gaussians for any constant k"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"

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)