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 "geometric estimation of multivariate dependency"):
  Abstract: This paper proposes a geometric estimator of dependency between a pair of multivariate random variables. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual information (GMI), which is equivalent to the Henze–Penrose divergence. between the joint distribution of the multivariate samples and the product of the marginals. The GMI has many of the same properties as standard MI but can be estimated from empirical data without density estimation; making it scalable to large datasets. The proposed empirical estimator of GMI is simple to implement, involving the construction of an minimal spanning tree (MST) spanning over both the original data and a randomly permuted version of this data. We establish asymptotic convergence of the estimator and convergence rates of the bias and variance for smooth multivariate density functions belonging to a Holder class. We demonstrate the advantages of our proposed geometric dependency estimator in a series of experiments.

Includes members:
  Member "a dimension independent discriminant between distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to bound the multi class bayes error")
  Member "convergence rates for empirical estimation of binary classification bounds":
    also connected through other hyperedges:
      (none)
  Member "scalable mutual information estimation using dependence graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "to tune or not to tune adapting pretrained representations to diverse tasks")
  Member "direct estimation of information divergence using nearest neighbor ratios":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "information theoretic structure learning with confidence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to bound the multi class bayes error")
  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 "improving convergence of divergence functional ensemble estimators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to bound the multi class bayes error")
  Member "a review of feature selection methods based on mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "feature selection and feature extraction in pattern analysis a literature review")

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.LG(Machine Learning)