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: "stochastic backpropagation through mixture density distributions"
  Abstract: The ability to backpropagate stochastic gradients through continuous latent distributions has been crucial to the emergence of variational autoencoders and stochastic gradient variational Bayes. The key ingredient is an unbiased and low-variance way of estimating gradients with respect to distribution parameters from gradients evaluated at distribution samples. The "reparameterization trick" provides a class of transforms yielding such estimators for many continuous distributions, including the Gaussian and other members of the location-scale family. However the trick does not readily extend to mixture density models, due to the difficulty of reparameterizing the discrete distribution over mixture weights. This report describes an alternative transform, applicable to any continuous multivariate distribution with a differentiable density function from which samples can be drawn, and uses it to derive an unbiased estimator for mixture density weight derivatives. Combined with the reparameterization trick applied to the individual mixture components, this estimator makes it straightforward to train variational autoencoders with mixture-distributed latent variables, or to perform stochastic variational inference with a mixture density variational posterior.

Connected through hyperedge HE-1 (induced by source paper "adversarial attacks on probabilistic autoregressive forecasting models"):
  with members:
    - "adversarial learning of deepfakes in accounting"
    - "adversarial recovery of agent rewards from latent spaces of the limit order book"
    - "implicit reparameterization gradients"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "conditional image generation with pixelcnn decoders"
    - "towards evaluating the robustness of neural networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "automatic differentiation variational inference with mixtures"):
  with members:
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "implicit reparameterization gradients"
    - "modeling uncertainty with hedged instance embedding"
    - "on calibration of modern neural networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "vae with a vampprior"
    - "deep variational information bottleneck"
    - "importance weighted autoencoders"

Connected through hyperedge HE-3 (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"
    - "adversarial distillation of bayesian neural network posteriors"
    - "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"

Connected through hyperedge HE-4 (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"
    - "bayesian recurrent 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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "generalized transformation based gradient"):
  with members:
    - "implicit reparameterization gradients"
    - "gradient estimation using stochastic computation graphs"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-6 (induced by source paper "seq2seq mimic games a signaling perspective"):
  with members:
    - "proximal policy optimization and its dynamic version for sequence generation"
    - "emergent communication through negotiation"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "adversarial evaluation of dialogue models"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "multi agent cooperation and the emergence of natural language"
    - "data generation as sequential decision making"

Connected through hyperedge HE-7 (induced by source paper "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "bayesian recurrent 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"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "doubly reparameterized gradient estimators for monte carlo objectives"):
  with members:
    - "implicit reparameterization gradients"
    - "stochastic video generation with a learned prior"
    - "online learning rate adaptation with hypergradient descent"
    - "stochastic variational video prediction"
    - "pixelvae a latent variable model for natural images"
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"
    - "learning wake sleep recurrent attention models"

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.NE(Neural and Evolutionary Computing)