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: "inference compilation and universal probabilistic programming"
  Abstract: We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of inference" because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a trained neural network denoted in a neural network specification language. When at test time this neural network is fed observational data and executed, it performs approximate inference in the original model specified by the probabilistic program. Our training objective and learning procedure are designed to allow the trained neural network to be used as a proposal distribution in a sequential importance sampling inference engine. We illustrate our method on mixture models and Captcha solving and show significant speedups in the efficiency of inference.

Connected through hyperedge HE-1 (induced by source paper "learning compositional rules via neural program synthesis"):
  with members:
    - "learning to infer program sketches"
    - "learning to prove theorems via interacting with proof assistants"
    - "human few shot learning of compositional instructions"
    - "compositional generalization through meta sequence to sequence learning"
    - "write execute assess program synthesis with a repl"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-2 (induced by source paper "etalumis bringing probabilistic programming to scientific simulators at scale"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "pyro deep universal probabilistic programming"
    - "an empirical model of large batch training"
    - "measuring the effects of data parallelism on neural network training"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "scaling sgd batch size to 32k for imagenet training"
    - "carla an open urban driving simulator"
    - "improvements to inference compilation for probabilistic programming in large scale scientific simulators"

Connected through hyperedge HE-3 (induced by source paper "multiverse causal reasoning using importance sampling in probabilistic programming"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "pyro deep universal probabilistic programming"
    - "local rule based explanations of black box decision systems"
    - "counterfactual multi agent policy gradients"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-4 (induced by source paper "towards verified stochastic variational inference for probabilistic programs"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"
    - "a domain theory for statistical probabilistic programming"
    - "existence and continuity of differential entropy for a class of distributions"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "cantor meets scott semantic foundations for probabilistic networks"
    - "deriving probability density functions from probabilistic functional programs"

Connected through hyperedge HE-5 (induced by source paper "learning to infer program sketches"):
  with members:
    - "ain t nobody got time for coding structure aware program synthesis from natural language"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "neural program search solving programming tasks from description and examples"
    - "coarse to fine decoding for neural semantic parsing"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "neural guided constraint logic programming for program synthesis"
    - "robustfill neural program learning under noisy i o"

Connected through hyperedge HE-6 (induced by source paper "meta amortized variational inference and learning"):
  with members:
    - "attentive neural processes"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "variational memory addressing in generative models"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "generative grading neural approximate parsing for automated student feedback"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reliable deep grade prediction with uncertainty estimation"
    - "automated essay scoring based on two stage learning"
    - "training classifiers with natural language explanations"
    - "multimodal generative models for scalable weakly supervised learning"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "attention is all you need"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-8 (induced by source paper "meta learning mcmc proposals"):
  with members:
    - "learning to optimize neural nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "signal based bayesian seismic monitoring"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "neurally guided procedural models amortized inference for procedural graphics programs using neural networks"
    - "importance weighted autoencoders"
    - "neural adaptive sequential monte carlo"

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.AI(Artificial Intelligence)