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 "minimax rate optimal estimation of divergences between discrete distributions"):
  Abstract: We refine the general methodology in [1] for the construction and analysis of essentially minimax estimators for a wide class of functionals of finite dimensional parameters, and elaborate on the case of discrete distributions with support size $S$ comparable with the number of observations $n$. Specifically, we determine the "smooth" and "non-smooth" regimes based on the confidence set and the smoothness of the functional. In the "non-smooth" regime, we apply an unbiased estimator for a suitable polynomial approximation of the functional. In the "smooth" regime, we construct a general version of the bias-corrected Maximum Likelihood Estimator (MLE) based on Taylor expansion. #R##N#We apply the general methodology to the problem of estimating the KL divergence between two discrete probability measures $P$ and $Q$ from empirical data in a non-asymptotic and possibly large alphabet setting. We construct minimax rate-optimal estimators for $D(P\|Q)$ when the likelihood ratio is upper bounded by a constant which may depend on the support size, and show that the performance of the optimal estimator with $n$ samples is essentially that of the MLE with $n\ln n$ samples. Our estimator is adaptive in the sense that it does not require the knowledge of the support size nor the upper bound on the likelihood ratio. We show that the general methodology results in minimax rate-optimal estimators for other divergences as well, such as the Hellinger distance and the $\chi^2$-divergence. Our approach refines the "Approximation" methodology recently developed for the construction of near minimax estimators of functionals of high-dimensional parameters, such as entropy, R\'enyi entropy, mutual information and $\ell_1$ distance in large alphabet settings, and shows that the "effective sample size enlargement" phenomenon holds significantly more widely than previously established.

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 "maximum likelihood estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "the complexity of estimating r enyi entropy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimax optimal additive functional estimation with discrete distribution slow divergence speed case")
  Member "minimax rates of entropy estimation on large alphabets via best polynomial approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "to split or not to split the impact of disparate treatment in classification")

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)