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: "ensemble estimation of distributional functionals via k nearest neighbors"
  Abstract: The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to their wide applicability in signal processing, information theory, machine learning, and statistics. In particular, $k$-nearest neighbor (nn) based methods have received a lot of attention due to their adaptive nature and their relatively low computational complexity. We derive the mean squared error (MSE) convergence rates of leave-one-out $k$-nn plug-in density estimators of a large class of distributional functionals without boundary correction. We then apply the theory of optimally weighted ensemble estimation to obtain weighted ensemble estimators that achieve the parametric MSE rate under assumptions that are competitive with the state of the art. The asymptotic distributions of these estimators, which are unknown for all other $k$-nn based distributional functional estimators, are also presented which enables us to perform hypothesis testing.

Connected through hyperedge HE-1 (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"
    - "estimating mutual information for discrete continuous mixtures"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"

Connected through hyperedge HE-2 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions"):
  with members:
    - "estimating mixture entropy with pairwise distances"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "multivariate f divergence estimation with confidence"
    - "k mle a fast algorithm for learning statistical mixture models"
    - "chernoff information of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"
    - "the burbea rao and bhattacharyya centroids"
    - "generalised pinsker inequalities"

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)