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 non parametric k nearest neighbour entropy estimator"
  Abstract: A non-parametric k-nearest neighbour based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering non-uniform probability densities in the region of k-nearest neighbours around each sample point. It aims at improving the classical estima-tors in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator and shown to be a significant improvement.

Connected through hyperedge HE-1 (induced by source paper "missing features reconstruction and its impact on classification accuracy"):
  with members:
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "non parametric estimation of mutual information with application to nonlinear optical fibers"):
  with members:
    - "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "capacity of a nonlinear optical channel with finite memory"
    - "mutual information as a figure of merit for optical fiber systems"

Connected through hyperedge HE-3 (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"
    - "justification of logarithmic loss via the benefit of side information"
    - "estimating mutual information by local gaussian approximation"

Connected through hyperedge HE-4 (induced by source paper "conditional dependence via shannon capacity axioms estimators and applications"):
  with members:
    - "demystifying fixed k nearest neighbor information estimators"
    - "telling cause from effect in deterministic linear dynamical systems"
    - "estimating mutual information by local gaussian approximation"
    - "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-5 (induced by source paper "breaking the bandwidth barrier geometrical adaptive entropy estimation"):
  with members:
    - "demystifying fixed k nearest neighbor information estimators"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-6 (induced by source paper "specific differential entropy rate estimation for continuous valued time series"):
  with members:
    - "estimating mutual information by local gaussian approximation"
    - "anatomy of a bit information in a time series observation"

Connected through hyperedge HE-7 (induced by source paper "estimating mutual information by local gaussian approximation"):
  with members:
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "ensemble estimation of multivariate f divergence"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

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)