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: "the complexity of estimating r enyi entropy"
  Abstract: It was recently shown that estimating the Shannon entropy $H(p)$ of a discrete $k$-symbol distribution $p$ requires $\Theta(k/\log k)$ samples, a number that grows near-linearly in the support size. In many applications $H(p)$ can be replaced by the more general R\'enyi entropy of order $\alpha$, $H_\alpha(p)$. We determine the number of samples needed to estimate $H_\alpha(p)$. 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. In particular, estimating $H_2(p)$, which arises in security, DNA reconstruction, closeness testing, and other applications, requires only $\Theta(\sqrt{k})$ samples. The estimators achieving these bounds are simple and run in time linear in the number of samples.

Connected through hyperedge HE-1 (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"
    - "minimax rate optimal estimation of divergences between 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"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-2 (induced by source paper "active distribution learning from indirect samples"):
  with members:
    - "adaptive estimation of shannon entropy"
    - "active sequential hypothesis testing"
    - "universal estimation of directed information"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "minimax rate optimal estimation of divergences between discrete distributions"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-5 (induced by source paper "justification of logarithmic loss via the benefit of side information"):
  with members:
    - "information measures the curious case of the binary alphabet"
    - "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"
    - "universal estimation of directed information"
    - "the relation between granger causality and directed information theory a review"
    - "improved information gain estimates for decision tree induction"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-6 (induced by source paper "semantically secure coding scheme achieving the capacity of a gaussian wiretap channel"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "equivocations exponents and second order coding rates under various r enyi information measures"
    - "converses for secret key agreement and secure computing"
    - "secret key agreement general capacity and second order asymptotics"
    - "a new upperbound for the oblivious transfer capacity of discrete memoryless channels"
    - "strong converse for a degraded wiretap channel via active hypothesis testing"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "principles of physical layer security in multiuser wireless networks a survey"

Connected through hyperedge HE-7 (induced by source paper "minimax estimation of functionals of discrete distributions"):
  with members:
    - "minimax estimation of kl divergence between discrete distributions"
    - "does dirichlet prior smoothing solve the shannon entropy estimation problem"
    - "adaptive estimation of shannon entropy"
    - "justification of logarithmic loss via the benefit of side information"
    - "large alphabet compression and predictive distributions through poissonization and tilting"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "minimax estimation of discrete distributions under ell_1 loss"

Connected through hyperedge HE-8 (induced by source paper "3d assisted image feature synthesis for novel views of an object"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)