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 gaussian process optimization with upper confidence bound and pure exploration"
  Abstract: In this paper, we consider the challenge of maximizing an unknown function f for which evaluations are noisy and are acquired with high cost. An iterative procedure uses the previous measures to actively select the next estimation of f which is predicted to be the most useful. We focus on the case where the function can be evaluated in parallel with batches of fixed size and analyze the benefit compared to the purely sequential procedure in terms of cumulative regret. We introduce the Gaussian Process Upper Confidence Bound and Pure Exploration algorithm (GP-UCB-PE) which combines the UCB strategy and Pure Exploration in the same batch of evaluations along the parallel iterations. We prove theoretical upper bounds on the regret with batches of size K for this procedure which show the improvement of the order of    $\sqrt{K}$    for fixed iteration cost over purely sequential versions. Moreover, the multiplicative constants involved have the property of being dimension-free. We also confirm empirically the efficiency of GP-UCB-PE on real and synthetic problems compared to state-of-the-art competitors.

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 "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"

Connected through hyperedge HE-3 (induced by source paper "regret bounds for batched bandits"):
  with members:
    - "batch policy learning under constraints"
    - "adaptivity in adaptive submodularity"
    - "introduction to multi armed bandits"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "stochastic submodular cover with limited adaptivity"
    - "delay and cooperation in nonstochastic bandits"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "online learning with switching costs and other adaptive adversaries"

Connected through hyperedge HE-4 (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"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "robots that can adapt like animals"

Connected through hyperedge HE-5 (induced by source paper "nonmyopic gaussian process optimization with macro actions"):
  with members:
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"

Connected through hyperedge HE-6 (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-7 (induced by source paper "bayesian optimization for iterative learning"):
  with members:
    - "practical multi fidelity bayesian optimization for hyperparameter tuning"
    - "bayesian optimization in alphago"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "accelerating neural architecture search using performance prediction"
    - "deep reinforcement learning that matters"
    - "deep bayesian active learning with image data"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"

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"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "knowledge matters importance of prior information for optimization"
    - "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)