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 estimation of functionals of discrete distributions"
  Abstract: We propose a general methodology for the construction and analysis of minimax estimators for a wide class of functionals of finite dimensional parameters, and elaborate on the case of discrete distributions, where the alphabet size $S$ is unknown and may be comparable with the number of observations $n$. We treat the respective regions where the functional is "nonsmooth" and "smooth" separately. In the "nonsmooth" regime, we apply an unbiased estimator for the best polynomial approximation of the functional whereas, in the "smooth" regime, we apply a bias-corrected Maximum Likelihood Estimator (MLE). We illustrate the merit of this approach by thoroughly analyzing two important cases: the entropy $H(P) = \sum_{i = 1}^S -p_i \ln p_i$ and $F_\alpha(P) = \sum_{i = 1}^S p_i^\alpha,\alpha>0$. We obtain the minimax $L_2$ rates for estimating these functionals. In particular, we demonstrate that our estimator achieves the optimal sample complexity $n \asymp S/\ln S$ for entropy estimation. We also show that the sample complexity for estimating $F_\alpha(P),0<\alpha<1$ is $n\asymp S^{1/\alpha}/ \ln S$, which can be achieved by our estimator but not the MLE. For $1<\alpha<3/2$, we show the minimax $L_2$ rate for estimating $F_\alpha(P)$ is $(n\ln n)^{-2(\alpha-1)}$ regardless of the alphabet size, while the $L_2$ rate for the MLE is $n^{-2(\alpha-1)}$. For all the above cases, the behavior of the minimax rate-optimal estimators with $n$ samples is essentially that of the MLE with $n\ln n$ samples. We highlight the practical advantages of our schemes for entropy and mutual information estimation. We demonstrate that our approach reduces running time and boosts the accuracy compared to existing various approaches. Moreover, we show that the mutual information estimator induced by our methodology leads to significant performance boosts over the Chow--Liu algorithm in learning graphical models.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "a general framework for symmetric property estimation"):
  with members:
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "approximate profile maximum likelihood"
    - "estimating the unseen from multiple populations"
    - "a unified maximum likelihood approach for optimal distribution property estimation"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-3 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

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 "differentially private anonymized histograms"):
  with members:
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "inspectre privately estimating the unseen"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "estimating renyi entropy of discrete distributions"
    - "on modeling profiles instead of values"

Connected through hyperedge HE-6 (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 rate optimal estimation of divergences between discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-7 (induced by source paper "hangul fonts dataset a hierarchical and compositional dataset for interrogating learned representations"):
  with members:
    - "deep learning as a tool for neural data analysis speech classification and cross frequency coupling in human sensorimotor cortex"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "style and content disentanglement in generative adversarial networks"
    - "emergence of invariance and disentanglement in deep representations"
    - "hierarchical multiscale recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "discovering hidden factors of variation in deep networks"

Connected through hyperedge HE-8 (induced by source paper "efficient profile maximum likelihood for universal symmetric property estimation"):
  with members:
    - "approximate profile maximum likelihood"
    - "estimating the unseen from multiple populations"
    - "minimax estimation of kl divergence between discrete distributions"
    - "a unified maximum likelihood approach for optimal distribution property estimation"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "on modeling profiles instead of values"

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)