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: "hybrid batch bayesian optimization"
  Abstract: Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm that, based on the current state, dynamically switches between a sequential policy and a batch policy with variable batch sizes. We provide theoretical justification for our algorithm and present experimental results on eight benchmark BO problems. The results show that our method achieves substantial speedup (up to %78) compared to a pure sequential policy, without suffering any significant performance loss.

Connected through hyperedge HE-1 (induced by source paper "on batch bayesian optimization"):
  with members:
    - "on kernelized multi armed bandits"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-2 (induced by source paper "on kernelized multi armed bandits"):
  with members:
    - "learning to optimize via posterior sampling"
    - "finite time analysis of kernelised contextual bandits"
    - "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"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-3 (induced by source paper "budgeted batch bayesian optimization with unknown batch sizes"):
  with members:
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"

Connected through hyperedge HE-4 (induced by source paper "heteroscedastic treed bayesian optimisation"):
  with members:
    - "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-5 (induced by source paper "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"):
  with members:
    - "online learning under delayed feedback"
    - "batch active learning via coordinated matching"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "near optimal nonmyopic value of information in graphical models"
    - "efficient optimal learning for contextual bandits"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "multi armed bandits in metric spaces"

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.AI(Artificial Intelligence)