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: "density functional estimators with k nearest neighbor bandwidths"
  Abstract: Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and information theory. Although kernel density estimators are widely used in practice for such functional estimation problems, practitioners are left on their own to choose an appropriate bandwidth for each application in hand. Further, kernel density estimators suffer from boundary biases, which are prevalent in real world data with lower dimensional structures. We propose using the fixed-k nearest neighbor distances for the bandwidth, which adaptively adjusts to local geometry. Further, we propose a novel estimator based on local likelihood density estimators, that mitigates the boundary biases. Although such a choice of fixed-k nearest neighbor distances to bandwidths results in inconsistent estimators, we provide a simple debiasing scheme that precomputes the asymptotic bias and divides off this term. With this novel correction, we show consistency of this debiased estimator. We provide numerical experiments suggesting that it improves upon competing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"

Connected through hyperedge HE-2 (induced by source paper "ensemble estimation of distributional functionals via k nearest neighbors"):
  with members:
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "direct ensemble estimation of density functionals"
    - "ensemble estimation of information divergence"
    - "improving convergence of divergence functional ensemble estimators"
    - "ensemble estimation of multivariate f divergence"
    - "efficient estimation of mutual information for strongly dependent variables"

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)