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 "learning to bound the multi class bayes error"):
  Abstract: In the context of supervised learning, meta learning uses features, metadata and other information to learn about the difficulty, behavior, or composition of the problem. Using this knowledge can be useful to contextualize classifier results or allow for targeted decisions about future data sampling. In this paper, we are specifically interested in learning the Bayes error rate (BER) based on a labeled data sample. Providing a tight bound on the BER that is also feasible to estimate has been a challenge. Previous work [1] has shown that a pairwise bound based on the sum of Henze-Penrose (HP) divergence over label pairs can be directly estimated using a sum of Friedman-Rafsky (FR) multivariate run test statistics. However, in situations in which the dataset and number of classes are large, this bound is computationally infeasible to calculate and may not be tight. Other multi-class bounds also suffer from computationally complex estimation procedures. In this paper, we present a generalized HP divergence measure that allows us to estimate the Bayes error rate with log-linear computation. We prove that the proposed bound is tighter than both the pairwise method and a bound proposed by Lin [2]. We also empirically show that these bounds are close to the BER. We illustrate the proposed method on the MNIST dataset, and show its utility for the evaluation of feature reduction strategies.

Includes members:
  Member "unsupervised meta learning for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving rubik s cube with a robot hand")
  Member "a dimension independent discriminant between distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geometric estimation of multivariate dependency")
  Member "scalable hash based estimation of divergence measures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable mutual information estimation using dependence graphs")
  Member "direct estimation of information divergence using nearest neighbor ratios":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "geometric estimation of multivariate dependency")
  Member "improving convergence of divergence functional ensemble estimators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geometric estimation of multivariate dependency")
  Member "nonparametric ensemble estimation of distributional functionals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics")
  Member "learning to classify with possible sensor failures":
    also connected through other hyperedges:
      (none)

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)