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: "improving convergence of divergence functional ensemble estimators"
  Abstract: Recent work has focused on the problem of nonparametric estimation of divergence functionals. Many existing approaches are restrictive in their assumptions on the density support or require difficult calculations at the support boundary which must be known a priori. We derive the MSE convergence rate of a leave-one-out kernel density plug-in divergence functional estimator for general bounded density support sets where knowledge of the support boundary is not required. We then generalize the theory of optimally weighted ensemble estimation to derive two estimators that achieve the parametric rate when the densities are sufficiently smooth. The asymptotic distribution of these estimators and some guidelines for tuning parameter selection are provided. Based on the theory, we propose an empirical estimator of R\'enyi-$\alpha$ divergence that outperforms the standard kernel density plug-in estimator, especially in high dimension.

Connected through hyperedge HE-1 (induced by source paper "geometric estimation of multivariate dependency"):
  with members:
    - "a dimension independent discriminant between distributions"
    - "convergence rates for empirical estimation of binary classification bounds"
    - "scalable mutual information estimation using dependence graphs"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of mutual information"
    - "a review of feature selection methods based on mutual information"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-2 (induced by source paper "learning to bound the multi class bayes error"):
  with members:
    - "unsupervised meta learning for reinforcement learning"
    - "a dimension independent discriminant between distributions"
    - "scalable hash based estimation of divergence measures"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "information theoretic structure learning with confidence"
    - "nonparametric ensemble estimation of distributional functionals"
    - "learning to classify with possible sensor failures"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-3 (induced by source paper "direct estimation of density functionals using a polynomial basis"):
  with members:
    - "minimax estimation of functionals of discrete distributions"
    - "information theoretical estimators toolbox"
    - "multivariate f divergence estimation with confidence"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "kernels on sample sets via nonparametric divergence estimates"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

Connected through hyperedge HE-4 (induced by source paper "scalable mutual information estimation using dependence graphs"):
  with members:
    - "scalable hash based estimation of divergence measures"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "strong data processing inequalities for channels and bayesian networks"
    - "opening the black box of deep neural networks via information"
    - "estimating mutual information for discrete continuous mixtures"
    - "estimating mixture entropy with pairwise distances"

Connected through hyperedge HE-5 (induced by source paper "scalable hash based estimation of divergence measures"):
  with members:
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-6 (induced by source paper "convergence rates for empirical estimation of binary classification bounds"):
  with members:
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "generalized exponential concentration inequality for r enyi divergence estimation"

Connected through hyperedge HE-7 (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "direct ensemble estimation of density functionals"
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of mutual information"
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mutual information for discrete continuous mixtures"
    - "ensemble estimation of information divergence"

Connected through hyperedge HE-8 (induced by source paper "ensemble estimation of mutual information"):
  with members:
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of information divergence"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "demystifying fixed k nearest neighbor information estimators"
    - "a review of feature selection methods based on mutual information"
    - "ensemble estimation of multivariate f divergence"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "changing the environment based on empowerment as intrinsic motivation"

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)