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 density estimation in near linear time using variable width histograms"
  Abstract: Let $p$ be an unknown and arbitrary probability distribution over $[0,1)$. We consider the problem of {\em density estimation}, in which a learning algorithm is given i.i.d. draws from $p$ and must (with high probability) output a hypothesis distribution that is close to $p$. The main contribution of this paper is a highly efficient density estimation algorithm for learning using a variable-width histogram, i.e., a hypothesis distribution with a piecewise constant probability density function. #R##N#In more detail, for any $k$ and $\epsilon$, we give an algorithm that makes $\tilde{O}(k/\epsilon^2)$ draws from $p$, runs in $\tilde{O}(k/\epsilon^2)$ time, and outputs a hypothesis distribution $h$ that is piecewise constant with $O(k \log^2(1/\epsilon))$ pieces. With high probability the hypothesis $h$ satisfies $d_{\mathrm{TV}}(p,h) \leq C \cdot \mathrm{opt}_k(p) + \epsilon$, where $d_{\mathrm{TV}}$ denotes the total variation distance (statistical distance), $C$ is a universal constant, and $\mathrm{opt}_k(p)$ is the smallest total variation distance between $p$ and any $k$-piecewise constant distribution. The sample size and running time of our algorithm are optimal up to logarithmic factors. The "approximation factor" $C$ in our result is inherent in the problem, as we prove that no algorithm with sample size bounded in terms of $k$ and $\epsilon$ can achieve $C<2$ regardless of what kind of hypothesis distribution it uses.

Connected through hyperedge HE-1 (induced by source paper "learning structured distributions from untrusted batches faster and simpler"):
  with members:
    - "optimal robust learning of discrete distributions from batches"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "efficiently learning structured distributions from untrusted batches"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "federated learning strategies for improving communication efficiency"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "learning discrete distributions from untrusted batches"

Connected through hyperedge HE-2 (induced by source paper "private hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

Connected through hyperedge HE-3 (induced by source paper "efficiently learning structured distributions from untrusted batches"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "high dimensional estimation via sum of squares proofs"
    - "federated learning strategies for improving communication efficiency"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning discrete distributions from untrusted batches"
    - "learning populations of parameters"

Connected through hyperedge HE-4 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "an efficient algorithm for high dimensional log concave maximum likelihood"
    - "density estimation for shift invariant multidimensional distributions"
    - "a polynomial time algorithm for maximum likelihood estimation of multivariate log concave densities"
    - "a size free clt for poisson multinomials and its applications"
    - "efficient robust proper learning of log concave distributions"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"

Connected through hyperedge HE-5 (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"
    - "sample efficient learning of mixtures"
    - "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"
    - "density estimation in linear time"

Connected through hyperedge HE-6 (induced by source paper "property testing for differential privacy"):
  with members:
    - "detecting violations of differential privacy"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"
    - "concentrated differential privacy"
    - "optimal testing for properties of distributions"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differential privacy and machine learning a survey and review"

Connected through hyperedge HE-7 (induced by source paper "near optimal closeness testing of discrete histogram distributions"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "a size free clt for poisson multinomials and its applications"
    - "fast algorithms for segmented regression"
    - "collision based testers are optimal for uniformity and closeness"
    - "learning multivariate log concave 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"

Connected through hyperedge HE-8 (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"

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)