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: "estimation of kl divergence optimal minimax rate"
  Abstract: The problem of estimating the Kullback-Leibler divergence $D(P\|Q)$ between two unknown distributions $P$ and $Q$ is studied, under the assumption that the alphabet size $k$ of the distributions can scale to infinity. The estimation is based on $m$ independent samples drawn from $P$ and $n$ independent samples drawn from $Q$. It is first shown that there does not exist any consistent estimator that guarantees asymptotically small worst-case quadratic risk over the set of all pairs of distributions. A restricted set that contains pairs of distributions, with density ratio bounded by a function $f(k)$ is further considered. {An augmented plug-in estimator is proposed, and its worst-case quadratic risk is shown to be within a constant factor of $(\frac{k}{m}+\frac{kf(k)}{n})^2+\frac{\log ^2 f(k)}{m}+\frac{f(k)}{n}$, if $m$ and $n$ exceed a constant factor of $k$ and $kf(k)$, respectively.} Moreover, the minimax quadratic risk is characterized to be within a constant factor of $(\frac{k}{m\log k}+\frac{kf(k)}{n\log k})^2+\frac{\log ^2 f(k)}{m}+\frac{f(k)}{n}$, if $m$ and $n$ exceed a constant factor of $k/\log(k)$ and $kf(k)/\log k$, respectively. The lower bound on the minimax quadratic risk is characterized by employing a generalized Le Cam's method. A minimax optimal estimator is then constructed by employing both the polynomial approximation and the plug-in approaches.

Connected through hyperedge HE-1 (induced by source paper "out of distribution detection with distance guarantee in deep generative models"):
  with members:
    - "conditional generative models are not robust"
    - "are generative deep models for novelty detection truly better"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"

Connected through hyperedge HE-2 (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-3 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions"):
  with members:
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "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-4 (induced by source paper "inhomogoenous hypergraph clustering with applications"):
  with members:
    - "on spectral partitioning of signed graphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "motif clustering and overlapping clustering for social network analysis"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "learning mixtures of ranking models"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "on the approximation of submodular functions"

Connected through hyperedge HE-5 (induced by source paper "approximate profile maximum likelihood"):
  with members:
    - "estimating the unseen from multiple populations"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-6 (induced by source paper "minimax rate optimal estimation of divergences between discrete distributions"):
  with members:
    - "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 "does dirichlet prior smoothing solve the shannon entropy estimation problem"):
  with members:
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "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"
    - "estimating the entropy of binary time series methodology some theory and a simulation study"

Connected through hyperedge HE-8 (induced by source paper "adaptive estimation of shannon entropy"):
  with members:
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "non asymptotic theory for the plug in rule in functional estimation"
    - "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)