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: "direct estimation of information divergence using nearest neighbor ratios"
  Abstract: We propose a direct estimation method for Renyi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets X and Y, respectively with N and M samples, where η := M/N is a constant value. Considering the k-nearest neighbor (k-NN) graph of Y in the joint data set (X, Y), we show that the average powered ratio of the number of X points to the number of Y points among all k-NN points is proportional to Renyi divergence of X and Y densities. A similar method can also be used to estimate f-divergence measures. We derive bias and variance rates, and show that for the class of γ-Holder smooth functions, the estimator achieves the MSE rate of O(N−2γ/(γ+d)). Furthermore, by using a weighted ensemble estimation technique, for density functions with continuous and bounded derivatives of up to the order d, and some extra conditions at the support set boundary, we derive an ensemble estimator that achieves the parametric MSE rate of O(1/N). Our estimator requires no boundary correction, and remarkably, the boundary issues do not show up. Our approach is also more computationally tractable than other competing estimators, which makes them appealing in many practical applications.

Connected through hyperedge HE-1 (induced by source paper "mint deep network compression via mutual information based neuron trimming"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "geometric estimation of multivariate dependency"
    - "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "scalable mutual information estimation using dependence graphs"):
  with members:
    - "scalable hash based estimation of divergence measures"
    - "strong data processing inequalities for channels and bayesian networks"
    - "opening the black box of deep neural networks via information"
    - "estimating mutual information for discrete continuous mixtures"
    - "estimating mixture entropy with pairwise distances"
    - "improving convergence of divergence functional ensemble estimators"

Connected through hyperedge HE-5 (induced by source paper "scalable hash based estimation of divergence measures"):
  with members:
    - "information theoretic structure learning with confidence"
    - "improving convergence of divergence functional ensemble estimators"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-6 (induced by source paper "convergence rates for empirical estimation of binary classification bounds"):
  with members:
    - "improving convergence of divergence functional ensemble estimators"
    - "generalized exponential concentration inequality for r enyi divergence estimation"

Connected through hyperedge HE-7 (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 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"
    - "improving convergence of divergence functional ensemble estimators"

Connected through hyperedge HE-8 (induced by source paper "ensemble estimation of distributional functionals via k nearest neighbors"):
  with members:
    - "direct ensemble estimation of density functionals"
    - "density functional estimators with k nearest neighbor bandwidths"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "ensemble estimation of multivariate f divergence"
    - "efficient estimation of mutual information for strongly dependent variables"

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)