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: "a nearly optimal and agnostic algorithm for properly learning a mixture of k gaussians for any constant k"
  Abstract: Learning a Gaussian mixture model (GMM) is a fundamental problem in machine learning, learning theory, and statistics. One notion of learning a GMM is proper learning: here, the goal is to find a mixture of $k$ Gaussians $\mathcal{M}$ that is close to the density $f$ of the unknown distribution from which we draw samples. The distance between $\mathcal{M}$ and $f$ is typically measured in the total variation or $L_1$-norm. #R##N#We give an algorithm for learning a mixture of $k$ univariate Gaussians that is nearly optimal for any fixed $k$. The sample complexity of our algorithm is $\tilde{O}(\frac{k}{\epsilon^2})$ and the running time is $(k \cdot \log\frac{1}{\epsilon})^{O(k^4)} + \tilde{O}(\frac{k}{\epsilon^2})$. It is well-known that this sample complexity is optimal (up to logarithmic factors), and it was already achieved by prior work. However, the best known time complexity for proper learning a $k$-GMM was $\tilde{O}(\frac{1}{\epsilon^{3k-1}})$. In particular, the dependence between $\frac{1}{\epsilon}$ and $k$ was exponential. We significantly improve this dependence by replacing the $\frac{1}{\epsilon}$ term with a $\log \frac{1}{\epsilon}$ while only increasing the exponent moderately. Hence, for any fixed $k$, the $\tilde{O} (\frac{k}{\epsilon^2})$ term dominates our running time, and thus our algorithm runs in time which is nearly-linear in the number of samples drawn. Achieving a running time of $\textrm{poly}(k, \frac{1}{\epsilon})$ for proper learning of $k$-GMMs has recently been stated as an open problem by multiple researchers, and we make progress on this question. #R##N#Moreover, our approach offers an agnostic learning guarantee: our algorithm returns a good GMM even if the distribution we are sampling from is not a mixture of Gaussians. To the best of our knowledge, our algorithm is the first agnostic proper learning algorithm for GMMs.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "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"
    - "testing shape restrictions of discrete distributions"

Connected through hyperedge HE-4 (induced by source paper "properly learning poisson binomial distributions in almost polynomial time"):
  with members:
    - "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"
    - "pseudorandomness via the discrete fourier transform"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "testing shape restrictions of discrete distributions"
    - "testing poisson binomial distributions"

Connected through hyperedge HE-5 (induced by source paper "sample optimal density estimation in nearly linear time"):
  with members:
    - "differentially private release and learning of threshold functions"
    - "tight bounds for learning a mixture of two gaussians"
    - "even faster integer multiplication"
    - "near optimal sample estimators for spherical gaussian mixtures"
    - "near optimal density estimation in near linear time using variable width histograms"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "efficient density estimation via piecewise polynomial approximation"
    - "a two round variant of em for gaussian mixtures"

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.DS(Data Structures and Algorithms)