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: "efficient gradient based inference through transformations between bayes nets and neural nets"
  Abstract: Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models can often be transformed into an instance of the other, by switching between centered and differentiable non-centered parameterizations of the latent variables. The choice of parameterization greatly influences the efficiency of gradient-based posterior inference; we show that they are often complementary to eachother, we clarify when each parameterization is preferred and show how inference can be made robust. In the non-centered form, a simple Monte Carlo estimator of the marginal likelihood can be used for learning the parameters. Theoretical results are supported by experiments.

Connected through hyperedge HE-1 (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"
    - "muprop unbiased backpropagation for stochastic neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "compositional generalization through meta sequence to sequence learning"):
  with members:
    - "learning to infer program sketches"
    - "the omniglot challenge a 3 year progress report"
    - "human few shot learning of compositional instructions"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "systematic generalization what is required and can it be learned"
    - "jump to better conclusions scan both left and right"

Connected through hyperedge HE-4 (induced by source paper "hybrid system identification using switching density networks"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "learning programmatically structured representations with perceptor gradients"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "verifiable reinforcement learning via policy extraction"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "semi supervisedly co embedding attributed networks"):
  with members:
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "semi supervised learning on graphs with generative adversarial nets"
    - "variational autoencoders for semi supervised text classification"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "stochastic blockmodels meet graph neural networks"):
  with members:
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "max margin nonparametric latent feature models for link prediction"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "sparse matrix variate gaussian process blockmodels for network modeling"

Connected through hyperedge HE-7 (induced by source paper "nonparametric topic modeling with neural inference"):
  with members:
    - "discovering discrete latent topics with neural variational inference"
    - "variational lossy autoencoder"
    - "generating sentences from a continuous space"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-8 (induced by source paper "dirichlet variational autoencoder"):
  with members:
    - "discovering discrete latent topics with neural variational inference"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative 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.LG(Machine Learning)