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: "exploiting compositionality to explore a large space of model structures"
  Abstract: The recent proliferation of richly structured probabilistic models raises the question of how to automatically determine an appropriate model for a dataset. We investigate this question for a space of matrix decomposition models which can express a variety of widely used models from unsupervised learning. To enable model selection, we organize these models into a context-free grammar which generates a wide variety of structures through the compositional application of a few simple rules. We use our grammar to generically and efficiently infer latent components and estimate predictive likelihood for nearly 2500 structures using a small toolbox of reusable algorithms. Using a greedy search over our grammar, we automatically choose the decomposition structure from raw data by evaluating only a small fraction of all models. The proposed method typically finds the correct structure for synthetic data and backs off gracefully to simpler models under heavy noise. It learns sensible structures for datasets as diverse as image patches, motion capture, 20 Questions, and U.S. Senate votes, all using exactly the same code.

Connected through hyperedge HE-1 (induced by source paper "neurally guided structure inference"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "robust text to sql generation with execution guided decoding"
    - "tree to tree neural networks for program translation"
    - "neural guided constraint logic programming for program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "neural sketch learning for conditional program generation"

Connected through hyperedge HE-2 (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"
    - "inference compilation and universal probabilistic programming"
    - "neurally guided procedural models amortized inference for procedural graphics programs using neural networks"
    - "importance weighted autoencoders"

Connected through hyperedge HE-3 (induced by source paper "bayesian synthesis of probabilistic programs for automatic data modeling"):
  with members:
    - "probabilistic data analysis with probabilistic programming"
    - "deepcoder learning to write programs"
    - "crosscat a fully bayesian nonparametric method for analyzing heterogeneous high dimensional data"
    - "neural programmer interpreters"
    - "neural turing machines"
    - "learning probabilistic programs"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-4 (induced by source paper "towards assessing the impact of bayesian optimization s own hyperparameters"):
  with members:
    - "auto keras an efficient neural architecture search system"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "learning to optimize"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-5 (induced by source paper "neural architecture optimization"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"

Connected through hyperedge HE-6 (induced by source paper "hidden physics models machine learning of nonlinear partial differential equations"):
  with members:
    - "machine learning of linear differential equations using gaussian processes"
    - "inferring solutions of differential equations using noisy multi fidelity data"
    - "deep multi fidelity gaussian processes"
    - "gaussian processes for big data"

Connected through hyperedge HE-7 (induced by source paper "progressive neural architecture search"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "neural block sampling"):
  with members:
    - "learning to optimize neural nets"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "signal based bayesian seismic monitoring"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "inference compilation and universal probabilistic programming"
    - "neurally guided procedural models amortized inference for procedural graphics programs using 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)