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: "estimating mutual information by local gaussian approximation"
  Abstract: Estimating mutual information (MI) from samples is a fundamental problem in statistics, machine learning, and data analysis. Recently it was shown that a popular class of non-parametric MI estimators perform very poorly for strongly dependent variables and have sample complexity that scales exponentially with the true MI. This undesired behavior was attributed to the reliance of those estimators on local uniformity of the underlying (and unknown) probability density function. Here we present a novel semi-parametric estimator of mutual information, where at each sample point, densities are {\em locally} approximated by a Gaussians distribution. We demonstrate that the estimator is asymptotically unbiased. We also show that the proposed estimator has a superior performance compared to several baselines, and is able to accurately measure relationship strengths over many orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "demystifying fixed k nearest neighbor information estimators"):
  with members:
    - "ensemble estimation of mutual information"
    - "improving convergence of divergence functional ensemble estimators"
    - "equivalence of additive combinatorial linear inequalities for shannon entropy and differential entropy"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "nonparametric ensemble estimation of distributional functionals"
    - "on additive combinatorial affine inequalities for shannon entropy and differential entropy"
    - "a non parametric k nearest neighbour entropy estimator"
    - "justification of logarithmic loss via the benefit of side information"

Connected through hyperedge HE-2 (induced by source paper "conditional dependence via shannon capacity axioms estimators and applications"):
  with members:
    - "demystifying fixed k nearest neighbor information estimators"
    - "a non parametric k nearest neighbour entropy estimator"
    - "telling cause from effect in deterministic linear dynamical systems"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

Connected through hyperedge HE-3 (induced by source paper "specific differential entropy rate estimation for continuous valued time series"):
  with members:
    - "a non parametric k nearest neighbour entropy estimator"
    - "anatomy of a bit information in a time series observation"

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.IT(Information Theory)