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: "a dimension independent discriminant between distributions"
  Abstract: Henze-Penrose divergence is a non-parametric divergence measure that can be used to estimate a bound on the Bayes error in a binary classification problem. In this paper, we show that a cross-match statistic based on optimal weighted matching can be used to directly estimate Henze-Penrose divergence. Unlike an earlier approach based on the Friedman-Rafsky minimal spanning tree statistic, the proposed method is dimension-independent. The new approach is evaluated using simulation and applied to real datasets to obtain Bayes error estimates.

Connected through hyperedge HE-1 (induced by source paper "geometric estimation of multivariate dependency"):
  with members:
    - "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"
    - "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"
    - "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"
    - "ensemble estimation of multivariate f divergence"

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)