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: "demystifying fixed k nearest neighbor information estimators"
  Abstract: Estimating mutual information from i.i.d. samples drawn from an unknown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is one proposed by Kraskov and St\"ogbauer and Grassberger (KSG) in 2004, and is nonparametric and based on the distances of each sample to its $k^{\rm th}$ nearest neighboring sample, where $k$ is a fixed small integer. Despite its widespread use (part of scientific software packages), theoretical properties of this estimator have been largely unexplored. In this paper we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the bias as a function of number of samples. We argue that the superior performance benefits of the KSG estimator stems from a curious "correlation boosting" effect and build on this intuition to modify the KSG estimator in novel ways to construct a superior estimator. As a byproduct of our investigations, we obtain nearly tight rates of convergence of the $\ell_2$ error of the well known fixed $k$ nearest neighbor estimator of differential entropy by Kozachenko and Leonenko.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis of image registration"):
  with members:
    - "estimators for multivariate information measures in general probability spaces"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "a deep metric for multimodal registration"
    - "fundamental limits in multi image alignment"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "fast fair regression via efficient approximations of mutual information"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "a general framework for fair regression"
    - "fairness in supervised learning an information theoretic approach"
    - "a convex framework for fair regression"
    - "estimating mutual information for discrete continuous mixtures"

Connected through hyperedge HE-3 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "universal outlying sequence detection for continuous observations"
    - "ensemble estimation of multivariate f divergence"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-4 (induced by source paper "model augmented estimation of conditional mutual information for feature selection"):
  with members:
    - "on variational bounds of mutual information"
    - "ensemble estimation of mutual information"
    - "estimating mutual information for discrete continuous mixtures"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-5 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"

Connected through hyperedge HE-6 (induced by source paper "data efficient mutual information neural estimator"):
  with members:
    - "bayesian model agnostic meta learning"
    - "formal limitations on the measurement of mutual information"
    - "probabilistic model agnostic meta learning"
    - "efficient neural architecture search via parameter sharing"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "deep learning for channel coding via neural mutual information estimation"):
  with members:
    - "deep reinforcement learning autoencoder with noisy feedback"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "mine mutual information neural estimation"
    - "an introduction to deep learning for the physical layer"
    - "adam a method for stochastic optimization"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "tighter variational representations of f divergences via restriction to probability measures"

Connected through hyperedge HE-8 (induced by source paper "ccmi classifier based conditional mutual information estimation"):
  with members:
    - "estimators for multivariate information measures in general probability spaces"
    - "on calibration of modern neural networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"
    - "conditional generative adversarial nets"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "learning module networks"
    - "obtaining calibrated probabilities from boosting"

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.LG(Machine Learning)