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: "probo a framework for using probabilistic programming in bayesian optimization"
  Abstract: Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Bayesian optimization (BO), which leverages probabilistic models for this task. Most BO today uses Gaussian processes (GPs), or a few other surrogate models. However, there is a broad set of Bayesian modeling techniques that we may want to use to capture complex systems and reduce the number of queries. Probabilistic programs (PPs) are modern tools that allow for flexible model composition, incorporation of prior information, and automatic inference. In this paper, we develop ProBO, a framework for BO using only standard operations common to most PPs. This allows a user to drop in an arbitrary PP implementation and use it directly in BO. To do this, we describe black box versions of popular acquisition functions that can be used in our framework automatically, without model-specific derivation, and show how to optimize these functions. We also introduce a model, which we term the Bayesian Product of Experts, that integrates into ProBO and can be used to combine information from multiple models implemented with different PPs. We show empirical results using multiple PP implementations, and compare against standard BO methods.

Connected through hyperedge HE-1 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"
    - "evaluating the search phase of neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "botorch programmable bayesian optimization in pytorch"):
  with members:
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "practical multi fidelity bayesian optimization for hyperparameter tuning"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "pyro deep universal probabilistic programming"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "constant time predictive distributions for gaussian processes"

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)