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: "information theoretic structure learning with confidence"
  Abstract: Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension. If these dependencies are assumed to follow a Markov factor graph model, this exploration process is called structure discovery. For discrete-valued samples, estimates of the information divergence over the parametric class of multinomial models lead to structure discovery methods whose mean squared error achieves parametric convergence rates as the sample size grows. However, a naive application of this method to continuous nonparametric multivariate models converges much more slowly. In this paper we introduce a new method for nonparametric structure discovery that uses weighted ensemble divergence estimators that achieve parametric convergence rates and obey an asymptotic central limit theorem that facilitates hypothesis testing and other types of statistical validation.

Connected through hyperedge HE-1 (induced by source paper "geometric estimation of multivariate dependency"):
  with members:
    - "a dimension independent discriminant between distributions"
    - "convergence rates for empirical estimation of binary classification bounds"
    - "scalable mutual information estimation using dependence graphs"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "ensemble estimation of mutual information"
    - "improving convergence of divergence functional ensemble estimators"
    - "a review of feature selection methods based on mutual information"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-2 (induced by source paper "learning to bound the multi class bayes error"):
  with members:
    - "unsupervised meta learning for reinforcement learning"
    - "a dimension independent discriminant between distributions"
    - "scalable hash based estimation of divergence measures"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "improving convergence of divergence functional ensemble estimators"
    - "nonparametric ensemble estimation of distributional functionals"
    - "learning to classify with possible sensor failures"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-3 (induced by source paper "scalable hash based estimation of divergence measures"):
  with members:
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "improving convergence of divergence functional ensemble estimators"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-4 (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "direct ensemble estimation of density functionals"
    - "ensemble estimation of mutual information"
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mutual information for discrete continuous mixtures"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"

Connected through hyperedge HE-5 (induced by source paper "ensemble estimation of mutual information"):
  with members:
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "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-6 (induced by source paper "direct estimation of information divergence using nearest neighbor ratios"):
  with members:
    - "improving convergence of divergence functional ensemble estimators"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "ensemble estimation of multivariate f divergence"

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)