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 mixture entropy with pairwise distances"
  Abstract: Mixture distributions arise in many parametric and non-parametric settings—for example, in Gaussian mixture models and in non-parametric estimation. It is often necessary to compute the entropy of a mixture, but, in most cases, this quantity has no closed-form expression, making some form of approximation necessary. We propose a family of estimators based on a pairwise distance function between mixture components, and show that this estimator class has many attractive properties. For many distributions of interest, the proposed estimators are efficient to compute, differentiable in the mixture parameters, and become exact when the mixture components are clustered. We prove this family includes lower and upper bounds on the mixture entropy. The Chernoff    α   -divergence gives a lower bound when chosen as the distance function, with the Bhattacharyaa distance providing the tightest lower bound for components that are symmetric and members of a location family. The Kullback–Leibler divergence gives an upper bound when used as the distance function. We provide closed-form expressions of these bounds for mixtures of Gaussians, and discuss their applications to the estimation of mutual information. We then demonstrate that our bounds are significantly tighter than well-known existing bounds using numeric simulations. This estimator class is very useful in optimization problems involving maximization/minimization of entropy and mutual information, such as MaxEnt and rate distortion problems.

Connected through hyperedge HE-1 (induced by source paper "understanding and enhancing mixed sample data augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "data augmentation using random image cropping and patching for deep cnns"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "natural adversarial examples"
    - "fast autoaugment"
    - "data augmentation revisited rethinking the distribution gap between clean and augmented data"
    - "human uncertainty makes classification more robust"
    - "data augmentation by pairing samples for images classification"

Connected through hyperedge HE-2 (induced by source paper "on the effect of low rank weights on adversarial robustness of neural networks"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "sparse dnns with improved adversarial robustness"
    - "on generation of adversarial examples using convex programming"
    - "low rank structure of learned representations"
    - "opening the black box of deep neural networks via information"
    - "a survey on deep learning in medical image analysis"
    - "nonlinear information bottleneck"
    - "are accuracy and robustness correlated"

Connected through hyperedge HE-3 (induced by source paper "disentangling redundancy for multi task pruning"):
  with members:
    - "branched multi task networks deciding what layers to share"
    - "multi task zipping via layer wise neuron sharing"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "compressing neural networks using the variational information bottleneck"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-4 (induced by source paper "entropy and mutual information in models of deep neural networks"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "opening the black box of deep neural networks via information"
    - "inference in deep networks in high dimensions"
    - "the mutual information in random linear estimation"

Connected through hyperedge HE-5 (induced by source paper "adaptive estimators show information compression in deep neural networks"):
  with members:
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "opening the black box of deep neural networks via information"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "swish a self gated activation function"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning in neural networks"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-6 (induced by source paper "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"):
  with members:
    - "formal limitations on the measurement of mutual information"
    - "information theoretic co training"
    - "protecting sensory data against sensitive inferences"
    - "representation learning with contrastive predictive coding"
    - "graph based predictable feature analysis"
    - "unsupervised visual representation learning by context prediction"
    - "character aware neural language models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "the role of a layer in deep neural networks a gaussian process perspective"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "estimating information flow in neural networks"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning and the information bottleneck principle"
    - "difference target propagation"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "on the achievable rate and capacity for a sample based practical photon counting receiver"):
  with members:
    - "clipping noise and power allocation for ofdm based optical wireless communication using photon detection"
    - "superposition coding is almost always optimal for the poisson broadcast channel"
    - "a refined analysis of the poisson channel in the high photon efficiency regime"

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)