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 cross validation via sequential testing"
  Abstract: With the increasing size of today's data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses nonparametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of the full cross-validation. Theoretical considerations underline the statistical power of our procedure. The experimental evaluation shows that our method reduces the computation time by a factor of up to 120 compared to a full cross-validation with a negligible impact on the accuracy.

Connected through hyperedge HE-1 (induced by source paper "best arm identification in multi armed bandits with delayed feedback"):
  with members:
    - "online learning under delayed feedback"

Connected through hyperedge HE-2 (induced by source paper "efficient hyperparameter optimization and infinitely many armed bandits"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "a stratified analysis of bayesian optimization methods"
    - "thoughts on massively scalable gaussian processes"
    - "least squares revisited scalable approaches for multi class prediction"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "sharp analysis of low rank kernel matrix approximations"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "progressive sampling based bayesian optimization for efficient and automatic machine learning model selection"):
  with members:
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "selecting near optimal learners via incremental data allocation"
    - "generalization in adaptive data analysis and holdout reuse"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-4 (induced by source paper "hyperband a novel bandit based approach to hyperparameter optimization"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "thoughts on massively scalable gaussian processes"
    - "simple regret for infinitely many armed bandits"
    - "least squares revisited scalable approaches for multi class prediction"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "convolutional neural networks applied to house numbers digit classification"
    - "efficient multi start strategies for local search algorithms"

Connected through hyperedge HE-5 (induced by source paper "fast bayesian optimization of machine learning hyperparameters on large datasets"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "openml networked science in machine learning"
    - "expectation propagation for approximate bayesian inference"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)