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: "fast bayesian optimization of machine learning hyperparameters on large datasets"
  Abstract: Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single configuration often takes hours, days, or even weeks, which limits the achievable performance. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed Fabolas, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that Fabolas often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods or the recently proposed bandit strategy Hyperband.

Connected through hyperedge HE-1 (induced by source paper "a system for massively parallel hyperparameter tuning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "a generalized framework for population based training"
    - "darts differentiable architecture search"
    - "tune a research platform for distributed model selection and training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "regularizing and optimizing lstm language models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-2 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-3 (induced by source paper "a constructive prediction of the generalization error across scales"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "on the convergence rate of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "hark side of deep learning from grad student descent to automated machine learning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "do imagenet classifiers generalize to imagenet"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
    - "regularized evolution for image classifier architecture search"
    - "universal approximations of invariant maps by neural networks"
    - "do cifar 10 classifiers generalize to cifar 10"

Connected through hyperedge HE-5 (induced by source paper "fast efficient hyperparameter tuning for policy gradients"):
  with members:
    - "meta gradient reinforcement learning"
    - "bayesian optimization in alphago"
    - "deep variational reinforcement learning for pomdps"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "deep reinforcement learning that matters"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "random search and reproducibility for neural architecture search"):
  with members:
    - "learnable embedding space for efficient neural architecture compression"
    - "massively parallel hyperparameter tuning"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-7 (induced by source paper "botorch programmable bayesian optimization in pytorch"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "capacity allocation analysis of neural networks a tool for principled architecture design"):
  with members:
    - "capacity allocation through neural network layers"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "deepbase deep inspection of neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "efficient neural audio synthesis"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "parallel wavenet fast high fidelity speech synthesis"

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)