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 "does dirichlet prior smoothing solve the shannon entropy estimation problem"):
  Abstract: The Dirichlet prior is widely used in estimating discrete distributions and functionals of discrete distributions. In terms of Shannon entropy estimation, one approach is to plug-in the Dirichlet prior smoothed distribution into the entropy functional, while the other one is to calculate the Bayes estimator for entropy under the Dirichlet prior for squared error, which is the conditional expectation. We show that in general they do \emph{not} improve over the maximum likelihood estimator, which plugs-in the empirical distribution into the entropy functional. No matter how we tune the parameters in the Dirichlet prior, this approach cannot achieve the minimax rates in entropy estimation, as recently characterized by Jiao, Venkat, Han, and Weissman, and Wu and Yang. The performance of the minimax rate-optimal estimator with $n$ samples is essentially \emph{at least} as good as that of the Dirichlet smoothed entropy estimators with $n\ln n$ samples. #R##N#We harness the theory of approximation using positive linear operators for analyzing the bias of plug-in estimators for general functionals under arbitrary statistical models, thereby further consolidating the interplay between these two fields, which was thoroughly developed and exploited by Jiao, Venkat, Han, and Weissman. We establish new results in approximation theory, and apply them to analyze the bias of the Dirichlet prior smoothed plug-in entropy estimator. This interplay between bias analysis and approximation theory is of relevance and consequence far beyond the specific problem setting in this paper.

Includes members:
  Member "estimation of kl divergence optimal minimax rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "out of distribution detection with distance guarantee in deep generative models")
  Member "minimax rate optimal estimation of divergences between discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "credible sample elicitation")
  Member "adaptive estimation of shannon entropy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "minimax estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "maximum likelihood estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "minimax rates of entropy estimation on large alphabets via best polynomial approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "estimating the entropy of binary time series methodology some theory and a simulation study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on a class of markov order estimators based on ppm and other universal codes")

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)