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: "nonparametric ensemble estimation of distributional functionals"
  Abstract: Distributional functionals are integral functionals of one or more probability distributions. Distributional functionals include information measures such as entropy, mutual information, and divergence. Recent work has focused on the problem of nonparametric estimation of entropy and information divergence functionals. Many existing approaches are restrictive in their assumptions on the density support set or require difficult calculations at the support boundary which must be known a priori. The MSE convergence rate of a leave-one-out kernel density plug-in divergence functional estimator for general bounded density support sets is derived where knowledge of the support boundary is not required. The theory of optimally weighted ensemble estimation is generalized 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, an empirical estimator of R\'enyi-$\alpha$ divergence is proposed that outperforms the standard kernel density plug-in estimator, especially in high dimension. The estimators are shown to be robust to the choice of tuning parameters.

Connected through hyperedge HE-1 (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"
    - "improving convergence of divergence functional ensemble estimators"
    - "learning to classify with possible sensor failures"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-2 (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-3 (induced by source paper "information theoretic structure learning with confidence"):
  with members:
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "demystifying fixed k nearest neighbor information estimators"
    - "ensemble estimation of multivariate f divergence"
    - "multivariate f divergence estimation with confidence"

Connected through hyperedge HE-4 (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"
    - "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"
    - "estimating mutual information by local gaussian approximation"

Connected through hyperedge HE-5 (induced by source paper "improving convergence of divergence functional ensemble estimators"):
  with members:
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "meta learning of bounds on the bayes classifier error"
    - "ensemble estimation of multivariate f divergence"
    - "empirically estimable classification bounds based on a new divergence measure"
    - "multivariate f divergence estimation with confidence"

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)