human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "generalized exponential concentration inequality for r enyi divergence estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the estimation of information measures of continuous distributions")
  Member "nonparametric ensemble estimation of distributional functionals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to bound the multi class bayes error")
  Member "meta learning of bounds on the bayes classifier error":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularization via structural label smoothing")
  Member "ensemble estimation of multivariate f divergence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions")
  Member "empirically estimable classification bounds based on a new divergence measure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning of bounds on the bayes classifier error")
  Member "multivariate f divergence estimation with confidence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geometric estimation of multivariate dependency")

Question: Please tell me which class the source paper 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)