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: "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
  Abstract: BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.

Connected through hyperedge HE-1 (induced by source paper "automated machine learning state of the art and open challenges"):
  with members:
    - "learning internal representations"
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "regularized evolution for image classifier architecture search"
    - "rafiki machine learning as an analytics service system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-2 (induced by source paper "optimization and abstraction a synergistic approach for analyzing neural network robustness"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "an overview of vulnerabilities of voice controlled systems"
    - "robust physical world attacks on deep learning models"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "output range analysis for deep neural networks"
    - "towards practical verification of machine learning the case of computer vision systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"

Connected through hyperedge HE-3 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "control regularization for reduced variance reinforcement learning"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "neural lander stable drone landing control using learned dynamics"
    - "spectral normalization for generative adversarial networks"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-4 (induced by source paper "λopt learn to regularize recommender models in finer levels"):
  with members:
    - "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "entity embeddings of categorical variables"
    - "a generic coordinate descent framework for learning from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-6 (induced by source paper "lipschitz bandit optimization with improved efficiency"):
  with members:
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "learning to speed up query planning in graph databases"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "taming subgraph isomorphism for rdf query processing"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-8 (induced by source paper "a successive elimination approach to adaptive robotic source seeking"):
  with members:
    - "model based active source identification in complex environments"
    - "multi armed bandits with metric movement costs"
    - "ergodic exploration of distributed information"
    - "best of k bandits"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

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)