human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "near optimal sample complexity bounds for robust learning of gaussians mixtures via compression schemes"):
  Abstract: We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance. This improves both the known upper bounds and lower bounds for this problem. For mixtures of axis-aligned Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice, matching a known lower bound. Moreover, these results hold in the agnostic-learning/robust-estimation setting as well, where the target distribution is only approximately a mixture of Gaussians. #R##N#The upper bound is shown using a novel technique for distribution learning based on a notion of `compression.' Any class of distributions that allows such a compression scheme can also be learned with few samples. Moreover, if a class of distributions has such a compression scheme, then so do the classes of products and mixtures of those distributions. The core of our main result is showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized compression scheme.

Includes members:
  Member "strong coresets for k median and subspace approximation goodbye dimension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coresets for clustering in graphs of bounded treewidth")
  Member "sample efficient learning of mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "agnostic estimation of mean and covariance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "robust estimators in high dimensions without the computational intractability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "learning multivariate log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families")
  Member "sample compression schemes for vc classes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal collusion free teaching")
  Member "near optimal sample estimators for spherical gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning sums of independent random variables with sparse collective support")

Question: Please tell me which class the source paper 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)