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: "deep amortized inference for probabilistic programs"
  Abstract: Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could try to learn from past inferences, so that future inferences run faster. This strategy is known as amortized inference; it has recently been applied to Bayesian networks and deep generative models. This paper proposes a system for amortized inference in PPLs. In our system, amortization comes in the form of a parameterized guide program. Guide programs have similar structure to the original program, but can have richer data flow, including neural network components. These networks can be optimized so that the guide approximately samples from the posterior distribution defined by the original program. We present a flexible interface for defining guide programs and a stochastic gradient-based scheme for optimizing guide parameters, as well as some preliminary results on automatically deriving guide programs. We explore in detail the common machine learning pattern in which a 'local' model is specified by 'global' random values and used to generate independent observed data points; this gives rise to amortized local inference supporting global model learning.

Connected through hyperedge HE-1 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "universal marginaliser for deep amortised inference for probabilistic programs"):
  with members:
    - "learning about an exponential amount of conditional distributions"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "pyro deep universal probabilistic programming"
    - "attention is all you need"
    - "using synthetic data to train neural networks is model based reasoning"
    - "inference compilation and universal probabilistic programming"
    - "made masked autoencoder for distribution estimation"
    - "neural adaptive sequential monte carlo"

Connected through hyperedge HE-4 (induced by source paper "probo versatile bayesian optimization using any probabilistic programming language"):
  with members:
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "inference compilation and universal probabilistic programming"
    - "neural architecture search with reinforcement learning"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-5 (induced by source paper "amortized rejection sampling in universal probabilistic programming"):
  with members:
    - "etalumis bringing probabilistic programming to scientific simulators at scale"
    - "efficient probabilistic inference in the quest for physics beyond the standard model"
    - "denotational validation of higher order bayesian inference"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-6 (induced by source paper "applying probabilistic programming to affective computing"):
  with members:
    - "multimodal generative models for scalable weakly supervised learning"
    - "a pragmatic theory of generic language"
    - "building machines that learn and think like people"
    - "church a language for generative models"

Connected through hyperedge HE-7 (induced by source paper "towards autoencoding variational inference for aspect based opinion summary"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "aspect based opinion summarization with convolutional neural networks"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-8 (induced by source paper "learning 3d shape completion under weak supervision"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional 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.AI(Artificial Intelligence)