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: "adaptive estimation of shannon entropy"
  Abstract: We consider estimating the Shannon entropy of a discrete distribution $P$ from $n$ i.i.d. samples. Recently, Jiao, Venkat, Han, and Weissman, and Wu and Yang constructed approximation theoretic estimators that achieve the minimax $L_2$ rates in estimating entropy. Their estimators are consistent given $n \gg \frac{S}{\ln S}$ samples, where $S$ is the alphabet size, and it is the best possible sample complexity. In contrast, the Maximum Likelihood Estimator (MLE), which is the empirical entropy, requires $n\gg S$ samples. #R##N#In the present paper we significantly refine the minimax results of existing work. To alleviate the pessimism of minimaxity, we adopt the adaptive estimation framework, and show that the minimax rate-optimal estimator in Jiao, Venkat, Han, and Weissman achieves the minimax rates simultaneously over a nested sequence of subsets of distributions $P$, without knowing the alphabet size $S$ or which subset $P$ lies in. In other words, their estimator is adaptive with respect to this nested sequence of the parameter space, which is characterized by the entropy of the distribution. We also characterize the maximum risk of the MLE over this nested sequence, and show, for every subset in the sequence, that the performance of the minimax rate-optimal estimator with $n$ samples is essentially that of the MLE with $n\ln n$ samples, thereby further substantiating the generality of the phenomenon identified by Jiao, Venkat, Han, and Weissman.

Connected through hyperedge HE-1 (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-2 (induced by source paper "active distribution learning from indirect samples"):
  with members:
    - "the complexity of estimating r enyi 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 "kernelized hashcode representations for relation extraction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "deep variational information bottleneck"
    - "a survey on learning to hash"
    - "orthogonal random features"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "learning structural kernels for natural language processing"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"

Connected through hyperedge HE-4 (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"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "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-5 (induced by source paper "density functional estimators with k nearest neighbor bandwidths"):
  with members:
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"
    - "demystifying fixed k nearest neighbor information estimators"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-6 (induced by source paper "does dirichlet prior smoothing solve the shannon entropy estimation problem"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "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-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"
    - "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"
    - "the complexity of estimating r enyi entropy"
    - "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 "maximum likelihood estimation of functionals of discrete distributions"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "justification of logarithmic loss via the benefit of side information"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "estimating renyi entropy of discrete distributions"

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)