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 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.

Includes members:
  Member "direct estimation of information divergence using nearest neighbor ratios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics")
  Member "density functional estimators with k nearest neighbor bandwidths":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "credible sample elicitation")
  Member "ensemble estimation of information divergence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a dimension independent discriminant between distributions")
  Member "improving convergence of divergence functional ensemble estimators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric estimation of multivariate dependency")
  Member "ensemble estimation of multivariate f divergence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions")
  Member "efficient estimation of mutual information for strongly dependent variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mint deep network compression via mutual information based neuron trimming")

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)