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: "muprop unbiased backpropagation for stochastic neural networks"
  Abstract: Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large parametric functions with that of graphical models, which makes it possible to learn very complex distributions. However, as backpropagation is not directly applicable to stochastic networks that include discrete sampling operations within their computational graph, training such networks remains difficult. We present MuProp, an unbiased gradient estimator for stochastic networks, designed to make this task easier. MuProp improves on the likelihood-ratio estimator by reducing its variance using a control variate based on the first-order Taylor expansion of a mean-field network. Crucially, unlike prior attempts at using backpropagation for training stochastic networks, the resulting estimator is unbiased and well behaved. Our experiments on structured output prediction and discrete latent variable modeling demonstrate that MuProp yields consistently good performance across a range of difficult tasks.

Connected through hyperedge HE-1 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "direct policy gradients direct optimization of policies in discrete action spaces"
    - "direct optimization through arg max for discrete variational auto encoder"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural machine translation with gumbel greedy decoding"
    - "classical structured prediction losses for sequence to sequence learning"

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"
    - "implicit reparameterization gradients"
    - "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"
    - "neural variational inference and learning in belief networks"

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"
    - "bayesian recurrent neural networks"
    - "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"

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"
    - "implicit reparameterization gradients"
    - "learning and querying fast generative models for reinforcement learning"

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 "cooperative image captioning"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "evaluating text to image matching using binary image selection bison"
    - "a neural compositional paradigm for image captioning"
    - "learning to describe differences between pairs of similar images"
    - "how agents see things on visual representations in an emergent language game"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "natural language does not emerge naturally in multi agent dialog"

Connected through hyperedge HE-7 (induced by source paper "td regularized actor critic methods"):
  with members:
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "f divergence constrained policy improvement"
    - "trust pcl an off policy trust region method for continuous control"
    - "boosting the actor with dual critic"

Connected through hyperedge HE-8 (induced by source paper "inherent weight normalization in stochastic neural networks"):
  with members:
    - "self normalizing neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "emnist an extension of mnist to handwritten letters"
    - "learning to learn by gradient descent by gradient descent"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "stochastic synapses enable efficient brain inspired learning machines"

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)