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 "one shot bayes opt with probabilistic population based training"):
  Abstract: Selecting optimal hyperparameters is a key challenge in machine learning. An exciting recent result showed it is possible to learn high-performing hyperparameter schedules on the fly in a single training run through methods inspired by Evolutionary Algorithms. These approaches have been shown to increase performance across a wide variety of machine learning tasks, ranging from supervised (SL) to reinforcement learning (RL). However, since they remain primarily evolutionary, they act in a greedy fashion, thus require a combination of vast computational resources and carefully selected meta-parameters to effectively explore the hyperparameter space. To address these shortcomings we look to Bayesian Optimization (BO), where a Gaussian Process surrogate model is combined with an acquisition function to produce a principled mechanism to trade off exploration vs exploitation. Our approach, which we call Probabilistic Population-Based Training ($\mathrm{P2BT}$), is able to transfer sample efficiency of BO to the online setting, making it possible to achieve these traits in a single training run. We show that $\mathrm{P2BT}$ is able to achieve high performance with only a small population size, making it useful for all researchers regardless of their computational resources.

Includes members:
  Member "a generalized framework for population based training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "emergent coordination through competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "towards explaining the regularization effect of initial large learning rate in training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "fast efficient hyperparameter tuning for policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aren assured relu nn architecture for model predictive control of lti systems")
  Member "bayesian optimization under heavy tailed payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online learning in kernelized markov decision processes")
  Member "massively parallel hyperparameter tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "bayesian optimization in alphago":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "tune a research platform for distributed model selection and training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)