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: "estimating mutual information for discrete continuous mixtures"
  Abstract: Estimating mutual information from observed samples is a basic primitive, useful in several machine learning tasks including correlation mining, information bottleneck clustering, learning a Chow-Liu tree, and conditional independence testing in (causal) graphical models. While mutual information is a well-defined quantity in general probability spaces, existing estimators can only handle two special cases of purely discrete or purely continuous pairs of random variables. The main challenge is that these methods first estimate the (differential) entropies of X, Y and the pair (X;Y) and add them up with appropriate signs to get an estimate of the mutual information. These 3H-estimators cannot be applied in general mixture spaces, where entropy is not well-defined. In this paper, we design a novel estimator for mutual information of discrete-continuous mixtures. We prove that the proposed estimator is consistent. We provide numerical experiments suggesting superiority of the proposed estimator compared to other heuristics of adding small continuous noise to all the samples and applying standard estimators tailored for purely continuous variables, and quantizing the samples and applying standard estimators tailored for purely discrete variables. This significantly widens the applicability of mutual information estimation in real-world applications, where some variables are discrete, some continuous, and others are a mixture between continuous and discrete components.

Connected through hyperedge HE-1 (induced by source paper "a theory of usable information under computational constraints"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "on variational bounds of mutual information"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "representation learning with contrastive predictive coding"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "censoring representations with an adversary"
    - "information theoretical estimators toolbox"

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"
    - "demystifying fixed k nearest neighbor information estimators"

Connected through hyperedge HE-3 (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"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "information theoretic feature transformation learning for brain interfaces"):
  with members:
    - "adversarial deep learning in eeg biometrics"
    - "hierarchical graphical models for context aware hybrid brain machine interfaces"
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "transfer learning in brain computer interfaces with adversarial variational autoencoders"
    - "information theoretic semi supervised metric learning via entropy regularization"

Connected through hyperedge HE-5 (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"
    - "demystifying fixed k nearest neighbor information estimators"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-6 (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"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"

Connected through hyperedge HE-7 (induced by source paper "causal confusion in imitation learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "monocular plan view networks for autonomous driving"
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "causal inference via kernel deviance measures"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "understanding the limitations of variational mutual information estimators"):
  with members:
    - "on mutual information maximization for representation learning"
    - "on variational bounds of mutual information"
    - "ccmi classifier based conditional mutual information estimation"
    - "wasserstein dependency measure for representation learning"
    - "formal limitations on the measurement of mutual information"
    - "towards a definition of disentangled representations"
    - "mine mutual information neural estimation"
    - "bias and generalization in deep generative models an empirical study"

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)