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: "implicit reparameterization gradients"
  Abstract: By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for training a variety of latent variable models. However, it is not applicable to a number of important continuous distributions. We introduce an alternative approach to computing reparameterization gradients based on implicit differentiation and demonstrate its broader applicability by applying it to Gamma, Beta, Dirichlet, and von Mises distributions, which cannot be used with the classic reparameterization trick. Our experiments show that the proposed approach is faster and more accurate than the existing gradient estimators for these distributions.

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"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "stochastic backpropagation through mixture density distributions"
    - "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 "generalized gumbel softmax gradient estimator for various discrete random variables"):
  with members:
    - "estimating gradients for discrete random variables by sampling without replacement"
    - "dirichlet variational autoencoder"
    - "discovering discrete latent topics with neural variational inference"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "tensorflow a system for large scale machine learning"
    - "neural variational inference for text processing"
    - "muprop unbiased backpropagation for stochastic neural networks"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-3 (induced by source paper "automatic differentiation variational inference with mixtures"):
  with members:
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "modeling uncertainty with hedged instance embedding"
    - "on calibration of modern neural networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "vae with a vampprior"
    - "stochastic backpropagation through mixture density distributions"
    - "deep variational information bottleneck"
    - "importance weighted autoencoders"

Connected through hyperedge HE-4 (induced by source paper "credit assignment techniques in stochastic computation graphs"):
  with members:
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-5 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "credit assignment techniques in stochastic computation graphs"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "new tricks for estimating gradients of expectations"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "clipped action policy gradient"
    - "understanding and correcting pathologies in the training of learned optimizers"

Connected through hyperedge HE-6 (induced by source paper "probabilistic models with deep neural networks"):
  with members:
    - "inferpy probabilistic modeling with deep neural networks made easy"
    - "pyro deep universal probabilistic programming"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "dice the infinitely differentiable monte carlo estimator"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "bayesian models of data streams with hierarchical power priors"

Connected through hyperedge HE-7 (induced by source paper "neuron ranking an informed way to condense convolutional neural networks architecture"):
  with members:
    - "flops as a direct optimization objective for learning sparse neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "a unified approach to interpreting model predictions"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning structured sparsity in deep neural networks"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "a unified framework for data poisoning attack to graph based semi supervised learning"):
  with members:
    - "adversarial attack on graph structured data"
    - "attack graph convolutional networks by adding fake nodes"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "data poisoning attacks against online learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "crafting adversarial input sequences for recurrent 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)