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: "ensemble estimation of mutual information"
  Abstract: We derive the mean squared error convergence rates of kernel density-based plug-in estimators of mutual information measures between two multidimensional random variables $\mathbf{X}$ and $\mathbf{Y}$ for two cases: 1) $\mathbf{X}$ and $\mathbf{Y}$ are both continuous; 2) $\mathbf{X}$ is continuous and $\mathbf{Y}$ is discrete. Using the derived rates, we propose an ensemble estimator of these information measures for the second case by taking a weighted sum of the plug-in estimators with varied bandwidths. The resulting ensemble estimator achieves the $1/N$ parametric convergence rate when the conditional densities of the continuous variables are sufficiently smooth. To the best of our knowledge, this is the first nonparametric mutual information estimator known to achieve the parametric convergence rate for this case, which frequently arises in applications (e.g. variable selection in classification). The estimator is simple to implement as it uses the solution to an offline convex optimization problem and simple plug-in estimators. A central limit theorem is also derived for the ensemble estimator. Ensemble estimators that achieve the parametric rate are also derived for the first case ($\mathbf{X}$ and $\mathbf{Y}$ are both continuous) and another case 3) $\mathbf{X}$ and $\mathbf{Y}$ may have any mixture of discrete and continuous components.

Connected through hyperedge HE-1 (induced by source paper "mint deep network compression via mutual information based neuron trimming"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "geometric estimation of multivariate dependency"
    - "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "learning adversarially robust representations via worst case mutual information maximization"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "robust classification using robust feature augmentation"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "a spectral view of adversarially robust features"
    - "adversarial spheres"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-3 (induced by source paper "model augmented estimation of conditional mutual information for feature selection"):
  with members:
    - "on variational bounds of mutual information"
    - "estimating mutual information for discrete continuous mixtures"
    - "demystifying fixed k nearest neighbor information estimators"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-4 (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"
    - "information theoretic structure learning with confidence"
    - "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-5 (induced by source paper "mine mutual information neural estimation"):
  with members:
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "it takes only two adversarial generator encoder networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "pacgan the power of two samples in generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "high resolution talking face generation via mutual information approximation"):
  with members:
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "which training methods for gans do actually converge"
    - "speech driven facial reenactment using conditional generative adversarial networks"
    - "obamanet photo realistic lip sync from text"
    - "recurrent scale approximation for object detection in cnn"
    - "you said that"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "context aware generative adversarial privacy"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic privacy with general distortion constraints"
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "information potential auto encoders"
    - "privacy aware mmse estimation"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "deep variational information bottleneck"

Connected through hyperedge HE-8 (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"
    - "information theoretic structure learning with confidence"
    - "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"

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)