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: "multivariate f divergence estimation with confidence"
  Abstract: The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics. While several nonparametric divergence estimators exist, relatively few have known convergence properties. In particular, even for those estimators whose MSE convergence rates are known, the asymptotic distributions are unknown. We establish the asymptotic normality of a recently proposed ensemble estimator of f-divergence between two distributions from a finite number of samples. This estimator has MSE convergence rate of O(1/T), is simple to implement, and performs well in high dimensions. This theory enables us to perform divergence-based inference tasks such as testing equality of pairs of distributions based on empirical samples. We experimentally validate our theoretical results and, as an illustration, use them to empirically bound the best achievable classification error.

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"
    - "improving convergence of divergence functional ensemble estimators"
    - "a review of feature selection methods based on mutual information"

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"
    - "improving convergence of divergence functional ensemble estimators"
    - "nonparametric ensemble estimation of distributional functionals"
    - "learning to classify with possible sensor failures"

Connected through hyperedge HE-3 (induced by source paper "direct estimation of density functionals using a polynomial basis"):
  with members:
    - "improving convergence of divergence functional ensemble estimators"
    - "minimax estimation of functionals of discrete distributions"
    - "information theoretical estimators toolbox"
    - "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 "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-5 (induced by source paper "ensemble estimation of mutual information"):
  with members:
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions"):
  with members:
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mixture entropy with pairwise distances"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "information theoretic structure learning with confidence"):
  with members:
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "nonparametric ensemble estimation of distributional functionals"
    - "demystifying fixed k nearest neighbor information estimators"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-8 (induced by source paper "improving convergence of divergence functional ensemble estimators"):
  with members:
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "nonparametric ensemble estimation of distributional functionals"
    - "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"

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)