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: "scalable bayesian learning of recurrent neural networks for language modeling"
  Abstract: Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach over stochastic optimization.

Connected through hyperedge HE-1 (induced by source paper "amagold amortized metropolis adjustment for efficient stochastic gradient mcmc"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "minibatch gibbs sampling on large graphical models"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "an efficient minibatch acceptance test for metropolis hastings"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-2 (induced by source paper "cyclical stochastic gradient mcmc for bayesian deep learning"):
  with members:
    - "averaging weights leads to wider optima and better generalization"
    - "snapshot ensembles train 1 get m for free"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "super convergence very fast training of residual networks using large learning rates"
    - "bayesian recurrent neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-3 (induced by source paper "characterizing membership privacy in stochastic gradient langevin dynamics"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "dropblock a regularization method for convolutional networks"
    - "generalization error bounds for noisy iterative algorithms"
    - "understanding membership inferences on well generalized learning models"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "privacy risk in machine learning analyzing the connection to overfitting"

Connected through hyperedge HE-4 (induced by source paper "confidence modeling for neural semantic parsing"):
  with members:
    - "neural semantic parsing over multiple knowledge bases"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "introducing noise in decentralized training of neural networks"):
  with members:
    - "communication efficient distributed online learning with kernels"
    - "effective parallelisation for machine learning"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "bayesian recurrent neural networks"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning to learn by gradient descent by gradient descent"
    - "stochastic backpropagation through mixture density distributions"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "learning to optimize"
    - "recurrent highway networks"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"

Connected through hyperedge HE-7 (induced by source paper "entropy sgd biasing gradient descent into wide valleys"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "understanding deep learning requires rethinking generalization"
    - "training recurrent neural networks by diffusion"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "recurrent batch normalization"
    - "bayesian dark knowledge"
    - "trivializing the energy landscape of deep 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.CL(Computation and Language)