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: "generalized exponential concentration inequality for r enyi divergence estimation"
  Abstract: Estimating divergences in a consistent way is of great importance in many machine learning tasks. Although this is a fundamental problem in nonparametric statistics, to the best of our knowledge there has been no finite sample exponential inequality convergence bound derived for any divergence estimators. The main contribution of our work is to provide such a bound for an estimator of R\'enyi-$\alpha$ divergence for a smooth H\"older class of densities on the $d$-dimensional unit cube $[0, 1]^d$. We also illustrate our theoretical results with a numerical experiment.

Connected through hyperedge HE-1 (induced by source paper "on the estimation of information measures of continuous distributions"):
  with members:
    - "on variational bounds of mutual information"
    - "formal limitations on the measurement of mutual information"
    - "representation learning with contrastive predictive coding"
    - "opening the black box of deep neural networks via information"
    - "estimating mutual information for discrete continuous mixtures"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "convergence rates for empirical estimation of binary classification bounds"):
  with members:
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "improving convergence of divergence functional ensemble estimators"

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"
    - "learning structural kernels for natural language processing"
    - "adaptive estimation of shannon entropy"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"

Connected through hyperedge HE-4 (induced by source paper "ensemble estimation of mutual information"):
  with members:
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "demystifying fixed k nearest neighbor information estimators"
    - "a review of feature selection methods based on mutual information"
    - "ensemble estimation of multivariate f divergence"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "changing the environment based on empowerment as intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "direct estimation of information divergence using nearest neighbor ratios"):
  with members:
    - "information theoretic structure learning with confidence"
    - "improving convergence of divergence functional ensemble estimators"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-6 (induced by source paper "on the complexity of estimating renyi divergences"):
  with members:
    - "testing shape restrictions of discrete distributions"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-7 (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"
    - "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"
    - "estimating mutual information by local gaussian approximation"

Connected through hyperedge HE-8 (induced by source paper "improving convergence of divergence functional ensemble estimators"):
  with members:
    - "nonparametric ensemble estimation of distributional functionals"
    - "meta learning of bounds on the bayes classifier error"
    - "ensemble estimation of multivariate f divergence"
    - "empirically estimable classification bounds based on a new divergence measure"
    - "multivariate f divergence estimation with confidence"

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)