human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sampling acquisition functions for batch bayesian optimization"):
  Abstract: This paper presents Acquisition Thompson Sampling (ATS), a novel algorithm for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model parameters. ATS is conceptually simple, straightforward to implement and, unlike other batch BO methods, it can be employed to parallelize any sequential acquisition function. In order to improve performance for multi-modal tasks, we show that ATS can be combined with existing techniques in order to realize different explore-exploit trade-offs and take into account pending function evaluations. We present experiments on a variety of benchmark functions and on the hyper-parameter optimization of a popular gradient boosting tree algorithm. These demonstrate the competitiveness of our algorithm with two state-of-the-art batch BO methods, and its advantages to classical parallel Thompson Sampling for BO.

Includes members:
  Member "on kernelized multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "batched gaussian process bandit optimization via determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regret bounds for batched bandits")
  Member "parallel predictive entropy search for batch global optimization of expensive objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one shot bayes opt with probabilistic population based training")
  Member "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parallel predictive entropy search for batch global optimization of expensive objective functions")
  Member "information theoretic regret bounds for gaussian process optimization in the bandit setting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "portfolio allocation for bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data")

Question: Please tell me which class the source paper 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)