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: "estimating renyi entropy of discrete distributions"
  Abstract: It was recently shown that estimating the Shannon entropy $H({\rm p})$ of a discrete $k$-symbol distribution ${\rm p}$ requires $\Theta(k/\log k)$ samples, a number that grows near-linearly in the support size. In many applications $H({\rm p})$ can be replaced by the more general R\'enyi entropy of order $\alpha$, $H_\alpha({\rm p})$. We determine the number of samples needed to estimate $H_\alpha({\rm p})$ for all $\alpha$, showing that $\alpha  1$ requires a near-linear $k$ samples, but, perhaps surprisingly, integer $\alpha>1$ requires only $\Theta(k^{1-1/\alpha})$ samples. Furthermore, developing on a recently established connection between polynomial approximation and estimation of additive functions of the form $\sum_{x} f({\rm p}_x)$, we reduce the sample complexity for noninteger values of $\alpha$ by a factor of $\log k$ compared to the empirical estimator. The estimators achieving these bounds are simple and run in time linear in the number of samples. Our lower bounds provide explicit constructions of distributions with different R\'enyi entropies that are hard to distinguish.

Connected through hyperedge HE-1 (induced by source paper "practical estimation of renyi entropy"):
  with members:
    - "multiple source adaptation and the renyi divergence"
    - "guessing revisited a large deviations approach"

Connected through hyperedge HE-2 (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-3 (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"
    - "minimax estimation of functionals of discrete distributions"
    - "extremal mechanisms for local differential privacy"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "on modeling profiles instead of values"

Connected through hyperedge HE-4 (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 estimation of functionals of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "distributed simulation and distributed inference"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "test without trust optimal locally private distribution testing"
    - "sample optimal identity testing with high probability"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "testing shape restrictions of discrete distributions"

Connected through hyperedge HE-6 (induced by source paper "sharp bounds for generalized uniformity testing"):
  with members:
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "fourier based testing for families of distributions"
    - "generalized uniformity testing"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "collision based testers are optimal for uniformity and closeness"

Connected through hyperedge HE-7 (induced by source paper "generalized uniformity testing"):
  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"
    - "testing identity of structured distributions"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "demystifying fixed k nearest neighbor information estimators"):
  with members:
    - "ensemble estimation of mutual information"
    - "improving convergence of divergence functional ensemble estimators"
    - "equivalence of additive combinatorial linear inequalities for shannon entropy and differential entropy"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "nonparametric ensemble estimation of distributional functionals"
    - "on additive combinatorial affine inequalities for shannon entropy and differential entropy"
    - "a non parametric k nearest neighbour entropy estimator"
    - "justification of logarithmic loss via the benefit of side information"

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)