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: "parallel predictive entropy search for batch global optimization of expensive objective functions"
  Abstract: We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first non-greedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.

Connected through hyperedge HE-1 (induced by source paper "one shot bayes opt with probabilistic population based training"):
  with members:
    - "a generalized framework for population based training"
    - "emergent coordination through competition"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "fast efficient hyperparameter tuning for policy gradients"
    - "bayesian optimization under heavy tailed payoffs"
    - "massively parallel hyperparameter tuning"
    - "bayesian optimization in alphago"
    - "tune a research platform for distributed model selection and training"

Connected through hyperedge HE-2 (induced by source paper "robust policy search for robot navigation with stochastic meta policies"):
  with members:
    - "fully distributed bayesian optimization with stochastic policies"
    - "bayesian optimisation under uncertain inputs"
    - "using simulation to improve sample efficiency of bayesian optimization for bipedal robots"
    - "practical bayesian optimization in the presence of outliers"
    - "unscented bayesian optimization for safe robot grasping"
    - "funneled bayesian optimization for design tuning and control of autonomous systems"
    - "robots that can adapt like animals"
    - "heteroscedastic treed bayesian optimisation"

Connected through hyperedge HE-3 (induced by source paper "epsilon shotgun epsilon greedy batch bayesian optimisation"):
  with members:
    - "expected improvement versus predicted value in surrogate based optimization"
    - "sampling acquisition functions for batch bayesian optimization"
    - "greed is good exploration and exploitation trade offs in bayesian optimisation"
    - "efficient nonmyopic bayesian optimization and quadrature"
    - "bayesian optimization in alphago"
    - "active model learning and diverse action sampling for task and motion planning"

Connected through hyperedge HE-4 (induced by source paper "nonmyopic gaussian process optimization with macro actions"):
  with members:
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"

Connected through hyperedge HE-5 (induced by source paper "binoculars for efficient nonmyopic sequential experimental design"):
  with members:
    - "improving quadrature for constrained integrands"

Connected through hyperedge HE-6 (induced by source paper "population based augmentation efficient learning of augmentation policy schedules"):
  with members:
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "data augmentation by pairing samples for images classification"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "autoaugment learning augmentation policies from data"
    - "efficient neural architecture search via parameter sharing"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-7 (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-8 (induced by source paper "deep bo for hyperparameter optimization of deep networks"):
  with members:
    - "tapas train less accuracy predictor for architecture search"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "accelerating neural architecture search using performance prediction"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "knowledge matters importance of prior information for optimization"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "portfolio allocation for bayesian optimization"

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)