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: "adversarial distillation of bayesian neural network posteriors"
  Abstract: Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing samples from the BNN posterior using mini-batches. However, SGLD and its extensions require storage of many copies of the model parameters, a potentially prohibitive cost, especially for large neural networks. We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN). At test-time, samples are generated by the GAN. We show that this distillation framework incurs no loss in performance on recent BNN applications including anomaly detection, active learning, and defense against adversarial attacks. By construction, our framework not only distills the Bayesian predictive distribution, but the posterior itself. This allows one to compute quantities such as the approximate model variance, which is useful in downstream tasks. To our knowledge, these are the first results applying MCMC-based BNNs to the aforementioned downstream applications.

Connected through hyperedge HE-1 (induced by source paper "ensemble model patching a parameter efficient variational bayesian neural network"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "an empirical model of large batch training"
    - "a style based generator architecture for generative adversarial networks"
    - "averaging weights leads to wider optima and better generalization"
    - "k for the price of 1 parameter efficient multi task and transfer learning"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "your classifier is secretly an energy based model and you should treat it like one"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "conditional generative models are not robust"
    - "generative modeling by estimating gradients of the data distribution"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "spectral normalization for generative adversarial networks"
    - "invertible residual networks"

Connected through hyperedge HE-3 (induced by source paper "implicit posterior variational inference for deep gaussian processes"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "collective online learning of gaussian processes in massive multi agent systems"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational inference for bayesian sparse gaussian process regression"
    - "wavenet a generative model for raw audio"
    - "tensorflow a system for large scale machine learning"
    - "variational auto encoded deep gaussian processes"

Connected through hyperedge HE-4 (induced by source paper "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"):
  with members:
    - "policy optimization as wasserstein gradient flows"
    - "a convex duality framework for gans"
    - "on wasserstein reinforcement learning and the fokker planck equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "boosting the actor with dual critic"
    - "learning from conditional distributions via dual embeddings"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-5 (induced by source paper "hypergan a generative model for diverse performant neural networks"):
  with members:
    - "stochastic hyperparameter optimization through hypernetworks"
    - "dynamic filter networks"
    - "spatial transformer networks"
    - "the loss surfaces of multilayer networks"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-6 (induced by source paper "assessing the robustness of bayesian dark knowledge to posterior uncertainty"):
  with members:
    - "bayesian dark knowledge"

Connected through hyperedge HE-7 (induced by source paper "inhibited softmax for uncertainty estimation in neural networks"):
  with members:
    - "deep bayesian active semi supervised learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "noisy natural gradient as variational inference"
    - "robust physical world attacks on machine learning models"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "natural parameter networks a class of probabilistic neural networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

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)