human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "non asymptotic theory for the plug in rule in functional estimation"
  Abstract: The plug-in rule is widely used in estimating functionals of finite dimensional parameters, and involves plugging in an asymptotically efficient estimator for the parameter to obtain an asymptotically efficient estimator for the functional. We propose a general non-asymptotic theory for analyzing the performance of the plug-in rule, and demonstrate its utility by applying it to estimation of functionals of discrete distributions via the maximum likelihood estimator (MLE). #R##N#We show that existing theory is insufficient for analyzing the bias of the plug-in rule, and propose to apply the theory of approximation using positive linear operators to study this bias. The variance is controlled using the well-known tools from the literature on concentration inequalities. Our techniques completely characterize the maximum $L_2$ risk incurred by the MLE in estimating the Shannon entropy $H(P) = \sum_{i = 1}^S -p_i \ln p_i$, and $F_\alpha(P) = \sum_{i = 1}^S p_i^\alpha$ up to a constant. As corollaries, for Shannon entropy estimation, we show that it is necessary and sufficient to have $n = \omega(S)$ observations for the MLE to be consistent, where $S$ represents the alphabet size. In addition, we obtain that it is necessary and sufficient to consider $n = \omega(S^{1/\alpha})$ samples for the MLE to consistently estimate $F_\alpha(P), 0<\alpha<1$. The minimax sample complexity for both problems are $\omega(S/\ln S)$ and $\omega(S^{1/\alpha}/\ln S)$, which implies that the MLE is strictly sub-optimal. When $1<\alpha<3/2$, we show that the maximum $L_2$ rate of convergence for the MLE is $n^{-2(\alpha-1)}$ for infinite alphabet size, while the minimax $L_2$ rate is $(n\ln n)^{-2(\alpha-1)}$. When $\alpha\geq 3/2$, the MLE achieves the minimax optimal $L_2$ convergence rate $n^{-1}$ regardless of the alphabet size.

Connected through hyperedge HE-1 (induced by source paper "adaptive estimation of shannon entropy"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "minimax estimation of functionals of discrete distributions"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Question: Please tell me which class the center node 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)