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: "efficient profile maximum likelihood for universal symmetric property estimation"
  Abstract: Estimating symmetric properties of a distribution, e.g. support size, coverage, entropy, distance to uniformity, are among the most fundamental problems in algorithmic statistics. While each of these properties have been studied extensively and separate optimal estimators are known for each, in striking recent work, Acharya et al. 2016 showed that there is a single estimator that is competitive for all symmetric properties. This work proved that computing the distribution that approximately maximizes \emph{profile likelihood (PML)}, i.e. the probability of observed frequency of frequencies, and returning the value of the property on this distribution is sample competitive with respect to a broad class of estimators of symmetric properties. Further, they showed that even computing an approximation of the PML suffices to achieve such a universal plug-in estimator. Unfortunately, prior to this work there was no known polynomial time algorithm to compute an approximate PML and it was open to obtain a polynomial time universal plug-in estimator through the use of approximate PML. In this paper we provide a algorithm (in number of samples) that, given $n$ samples from a distribution, computes an approximate PML distribution up to a multiplicative error of $\exp(n^{2/3} \mathrm{poly} \log(n))$ in time nearly linear in $n$. Generalizing work of Acharya et al. 2016 on the utility of approximate PML we show that our algorithm provides a nearly linear time universal plug-in estimator for all symmetric functions up to accuracy $\epsilon = \Omega(n^{-0.166})$. Further, we show how to extend our work to provide efficient polynomial-time algorithms for computing a $d$-dimensional generalization of PML (for constant $d$) that allows for universal plug-in estimation of symmetric relationships between distributions.

Connected through hyperedge HE-1 (induced by source paper "a general framework for symmetric property estimation"):
  with members:
    - "approximate profile maximum likelihood"
    - "estimating the unseen from multiple populations"
    - "a unified maximum likelihood approach for optimal distribution property estimation"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-2 (induced by source paper "estimating entropy of distributions in constant space"):
  with members:
    - "communication and memory efficient testing of discrete distributions"
    - "detecting correlations with little memory and communication"
    - "minimax optimal estimators for additive scalar functionals of discrete distributions"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-3 (induced by source paper "unified sample optimal property estimation in near linear time"):
  with members:
    - "inspectre privately estimating the unseen"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "on modeling profiles instead of values"

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)