human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics"):
  Abstract: Mutual information is a measure of the dependence between random variables that has been used successfully in myriad applications in many fields. Generalized mutual information measures that go beyond classical Shannon mutual information have also received much interest in these applications. We derive the mean squared error convergence rates of kernel density-based plug-in estimators of general mutual information measures between two multidimensional random variables $\mathbf{X}$ and $\mathbf{Y}$ for two cases: 1) $\mathbf{X}$ and $\mathbf{Y}$ are continuous; 2) $\mathbf{X}$ and $\mathbf{Y}$ may have any mixture of discrete and continuous components. Using the derived rates, we propose an ensemble estimator of these information measures called GENIE by taking a weighted sum of the plug-in estimators with varied bandwidths. The resulting ensemble estimators achieve the $1/N$ parametric mean squared error convergence rate when the conditional densities of the continuous variables are sufficiently smooth. To the best of our knowledge, this is the first nonparametric mutual information estimator known to achieve the parametric convergence rate for the mixture case, which frequently arises in applications (e.g. variable selection in classification). The estimator is simple to implement and it uses the solution to an offline convex optimization problem and simple plug-in estimators. A central limit theorem is also derived for the ensemble estimators. We demonstrate the ensemble estimator for the mixed case on simulated data and apply the proposed estimator to analyze gene relationships in single cell data.

Includes members:
  Member "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the renyi capacity and center")
  Member "direct estimation of information divergence using nearest neighbor ratios":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "direct ensemble estimation of density functionals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ensemble estimation of distributional functionals via k nearest neighbors")
  Member "information theoretic structure learning with confidence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric estimation of multivariate dependency")
  Member "ensemble estimation of mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "ensemble estimation of distributional functionals via k nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions")
  Member "estimating mutual information for discrete continuous mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a theory of usable information under computational constraints")
  Member "ensemble estimation of information divergence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a dimension independent discriminant between distributions")

Question: Please tell me which class the source paper 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)