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: "bayesian recurrent neural networks"
  Abstract: In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.

Connected through hyperedge HE-1 (induced by source paper "uncertainty based class activation maps for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "ig rl inductive graph reinforcement learning for massive scale traffic signal control"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "colight learning network level cooperation for traffic signal control"
    - "large scale traffic signal control using a novel multi agent reinforcement learning"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"

Connected through hyperedge HE-3 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "improving variational auto encoders using householder flow"

Connected through hyperedge HE-4 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-5 (induced by source paper "probabilistic framework for solving visual dialog"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "u cam visual explanation using uncertainty based class activation maps"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "curriculum based dropout discriminator for domain adaptation"
    - "attending to discriminative certainty for domain adaptation"
    - "looking back at labels a class based domain adaptation technique"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "deep gamblers learning to abstain with portfolio theory"):
  with members:
    - "one shot federated learning"
    - "selectivenet a deep neural network with an integrated reject option"
    - "on the convergence of adam and beyond"
    - "multimodal language analysis with recurrent multistage fusion"
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural machine translation and sequence to sequence models a tutorial"

Connected through hyperedge HE-8 (induced by source paper "a comprehensive guide to bayesian convolutional neural network with variational inference"):
  with members:
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "exploring sparsity in recurrent neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "curiosity driven exploration in deep reinforcement learning via bayesian neural networks"
    - "stochastic backpropagation through mixture density distributions"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"

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)