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: "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.

Connected through hyperedge HE-1 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "density functional estimators with k nearest neighbor bandwidths"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-2 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "demystifying fixed k nearest neighbor information estimators"
    - "universal outlying sequence detection for continuous observations"
    - "ensemble estimation of multivariate f divergence"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-3 (induced by source paper "defending against whitebox adversarial attacks via randomized discretization"):
  with members:
    - "adversarial attacks and defences competition"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial logit pairing"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"

Connected through hyperedge HE-4 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"

Connected through hyperedge HE-5 (induced by source paper "estimating entropy of distributions in constant space"):
  with members:
    - "communication and memory efficient testing of discrete distributions"
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "detecting correlations with little memory and communication"
    - "minimax optimal estimators for additive scalar functionals of discrete distributions"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-6 (induced by source paper "minimax optimal additive functional estimation with discrete distribution slow divergence speed case"):
  with members:
    - "on the complexity of estimating renyi divergences"
    - "minimax optimal estimators for additive scalar functionals of discrete distributions"
    - "does dirichlet prior smoothing solve the shannon entropy estimation problem"
    - "minimax estimation of functionals of discrete distributions"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "the complexity of estimating r enyi entropy"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-7 (induced by source paper "which distribution distances are sublinearly testable"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "optimal testing for properties of distributions"
    - "l p testing and learning of discrete distributions"
    - "testing identity of structured distributions"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "estimating the fundamental limits is easier than achieving the fundamental limits"):
  with members:
    - "estimating learnability in the sublinear data regime"
    - "minimax redundancy for markov chains with large state space"
    - "adaptive estimation of shannon entropy"
    - "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)