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: "flipout efficient pseudo independent weight perturbations on mini batches"
  Abstract: Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.

Connected through hyperedge HE-1 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise"):
  with members:
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation"):
  with members:
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information theoretic local minima characterization and regularization"
    - "the information complexity of learning tasks their structure and their distance"

Connected through hyperedge HE-3 (induced by source paper "uncertainty estimation for end to end learned dense stereo matching via probabilistic deep learning"):
  with members:
    - "cnn based cost volume analysis as confidence measure for dense matching"
    - "lightweight probabilistic deep networks"
    - "pyramid stereo matching network"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-4 (induced by source paper "interplay between optimization and generalization of stochastic gradient descent with covariance noise"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "a coordinate free construction of scalable natural gradient"
    - "three mechanisms of weight decay regularization"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "exploring generalization in deep learning"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"

Connected through hyperedge HE-5 (induced by source paper "variational federated multi task learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "oracle order robust adaptive continual learning"
    - "knowledge transfer in deep block modular neural networks"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-6 (induced by source paper "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"):
  with members:
    - "deep audio visual speech recognition"
    - "moments in time dataset one million videos for event understanding"
    - "learning to separate object sounds by watching unlabeled video"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "what actions are needed for understanding human actions in videos"

Connected through hyperedge HE-7 (induced by source paper "learning sparse networks using targeted dropout"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "excitation dropout encouraging plasticity in deep neural networks"
    - "learning to prune filters in convolutional neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "attention is all you need"
    - "structured probabilistic pruning for convolutional neural network acceleration"

Connected through hyperedge HE-8 (induced by source paper "a novel unsupervised post processing calibration method for dnns with robustness to domain shift"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "introspective classification with convolutional nets"
    - "end to end learning for self driving cars"
    - "overcoming catastrophic forgetting in neural networks"
    - "wide residual 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)