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: "sample efficient learning of mixtures"
  Abstract: We consider PAC learning of probability distributions (a.k.a. density estimation), where we are given an i.i.d. sample generated from an unknown target distribution, and want to output a distribution that is close to the target in total variation distance. Let $\mathcal F$ be an arbitrary class of probability distributions, and let $\mathcal{F}^k$ denote the class of $k$-mixtures of elements of $\mathcal F$. Assuming the existence of a method for learning $\mathcal F$ with sample complexity $m_{\mathcal{F}}(\epsilon)$, we provide a method for learning $\mathcal F^k$ with sample complexity $O({k\log k \cdot m_{\mathcal F}(\epsilon) }/{\epsilon^{2}})$. Our mixture learning algorithm has the property that, if the $\mathcal F$-learner is proper/agnostic, then the $\mathcal F^k$-learner would be proper/agnostic as well. #R##N#This general result enables us to improve the best known sample complexity upper bounds for a variety of important mixture classes. First, we show that the class of mixtures of $k$ axis-aligned Gaussians in $\mathbb{R}^d$ is PAC-learnable in the agnostic setting with $\widetilde{O}({kd}/{\epsilon ^ 4})$ samples, which is tight in $k$ and $d$ up to logarithmic factors. Second, we show that the class of mixtures of $k$ Gaussians in $\mathbb{R}^d$ is PAC-learnable in the agnostic setting with sample complexity $\widetilde{O}({kd^2}/{\epsilon ^ 4})$, which improves the previous known bounds of $\widetilde{O}({k^3d^2}/{\epsilon ^ 4})$ and $\widetilde{O}(k^4d^4/\epsilon ^ 2)$ in its dependence on $k$ and $d$. Finally, we show that the class of mixtures of $k$ log-concave distributions over $\mathbb{R}^d$ is PAC-learnable using $\widetilde{O}(d^{(d+5)/2}\epsilon^{-(d+9)/2}k)$ samples.

Connected through hyperedge HE-1 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians"):
  with members:
    - "em converges for a mixture of many linear regressions"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "on learning mixtures of well separated gaussians"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning mixtures of gaussians in high dimensions"
    - "tight bounds for learning a mixture of two gaussians"

Connected through hyperedge HE-2 (induced by source paper "on the sample complexity of learning sum product networks"):
  with members:
    - "bayesian learning of sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "efficient robust proper learning of log concave distributions"
    - "expectation maximization for sum product networks as exponential family mixture models"
    - "a unified approach for learning the parameters of sum product networks"
    - "on the expressive efficiency of sum product networks"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-3 (induced by source paper "the optimal approximation factor in density estimation"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "algorithmic stability for adaptive data analysis"
    - "optimal learners for multiclass problems"
    - "near optimal density estimation in near linear time using variable width histograms"
    - "density estimation in linear time"

Connected through hyperedge HE-4 (induced by source paper "learning mixtures of linear regressions with nearly optimal complexity"):
  with members:
    - "convergence analysis of two layer neural networks with relu activation"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "learning mixtures of gaussians in high dimensions"
    - "spectral experts for estimating mixtures of linear regressions"
    - "settling the polynomial learnability of mixtures of gaussians"

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"
    - "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"
    - "near optimal sample estimators for spherical gaussian mixtures"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

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)