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: "an informational approach to the global optimization of expensive to evaluate functions"
  Abstract: In many global optimization problems motivated by engineering applications, the number of function evaluations is severely limited by time or cost. To ensure that each evaluation contributes to the localization of good candidates for the role of global minimizer, a sequential choice of evaluation points is usually carried out. In particular, when Kriging is used to interpolate past evaluations, the uncertainty associated with the lack of information on the function can be expressed and used to compute a number of criteria accounting for the interest of an additional evaluation at any given point. This paper introduces minimizer entropy as a new Kriging-based criterion for the sequential choice of points at which the function should be evaluated. Based on \emph{stepwise uncertainty reduction}, it accounts for the informational gain on the minimizer expected from a new evaluation. The criterion is approximated using conditional simulations of the Gaussian process model behind Kriging, and then inserted into an algorithm similar in spirit to the \emph{Efficient Global Optimization} (EGO) algorithm. An empirical comparison is carried out between our criterion and \emph{expected improvement}, one of the reference criteria in the literature. Experimental results indicate major evaluation savings over EGO. Finally, the method, which we call IAGO (for Informational Approach to Global Optimization) is extended to robust optimization problems, where both the factors to be tuned and the function evaluations are corrupted by noise.

Connected through hyperedge HE-1 (induced by source paper "tuneful an online significance aware configuration tuner for big data analytics"):
  with members:
    - "flash a faster optimizer for sbse tasks"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "sensitivity analysis of expensive black box systems using metamodeling"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "student performance prediction with optimum multilabel ensemble model"):
  with members:
    - "a scikit based python environment for performing multi label classification"
    - "data mining a prediction of performer or underperformer using classification"

Connected through hyperedge HE-3 (induced by source paper "neural generative models for global optimization with gradients"):
  with members:
    - "generative adversarial networks for multimodal representation learning in video hyperlinking"
    - "the cma evolution strategy a tutorial"
    - "adam a method for stochastic optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-4 (induced by source paper "combining bayesian optimization and lipschitz optimization"):
  with members:
    - "improving the expected improvement algorithm"
    - "a literature survey of benchmark functions for global optimisation problems"

Connected through hyperedge HE-5 (induced by source paper "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"):
  with members:
    - "automatic lqr tuning based on gaussian process global optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"

Connected through hyperedge HE-6 (induced by source paper "hyperparameter optimization of deep neural networks using non probabilistic rbf surrogate model"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "flash fast bayesian optimization for data analytic pipelines"):
  with members:
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-8 (induced by source paper "automatic lqr tuning based on gaussian process global optimization"):
  with members:
    - "safe controller optimization for quadrotors with gaussian processes"

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.NA(Numerical Analysis)