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: "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"
  Abstract: Can one parallelize complex exploration exploitation tradeoffs? As an example, consider the problem of optimal high-throughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate function mapping stimulus to response and identify the maximum of the function. We formalize the task as a multi-armed bandit problem, where the unknown payoff function is sampled from a Gaussian process (GP), and instead of a single arm, in each round we pull a batch of several arms in parallel. We develop GP-BUCB, a principled algorithm for choosing batches, based on the GP-UCB algorithm for sequential GP optimization. We prove a surprising result; as compared to the sequential approach, the cumulative regret of the parallel algorithm only increases by a constant factor independent of the batch size B. Our results provide rigorous theoretical support for exploiting parallelism in Bayesian global optimization. We demonstrate the effectiveness of our approach on two real-world applications.

Connected through hyperedge HE-1 (induced by source paper "sampling acquisition functions for batch bayesian optimization"):
  with members:
    - "on kernelized multi armed bandits"
    - "xgboost a scalable tree boosting system"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-2 (induced by source paper "parallel predictive entropy search for batch global optimization of expensive objective functions"):
  with members:
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "bandits and experts in metric spaces"):
  with members:
    - "bandits with concave rewards and convex knapsacks"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"
    - "truthful mechanisms with implicit payment computation"
    - "ranked bandits in metric spaces learning optimally diverse rankings over large document collections"

Connected through hyperedge HE-4 (induced by source paper "parallel gaussian process optimization with upper confidence bound and pure exploration"):
  with members:
    - "bandit algorithms for tree search"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "joint optimization and variable selection of high dimensional gaussian processes"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "near optimal nonmyopic value of information in graphical models"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "online learning under delayed feedback"):
  with members:
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "auto weka combined selection and hyperparameter optimization of classification algorithms"):
  with members:
    - "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)