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: "portfolio allocation for bayesian optimization"
  Abstract: Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.

Connected through hyperedge HE-1 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "on kernelized multi armed bandits"
    - "context aware bandits"
    - "a gang of bandits"
    - "sparse gaussian processes for multi task learning"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "efficient rollout strategies for bayesian optimization"):
  with members:
    - "lookahead bayesian optimization via rollout guarantees and sequential rolling horizons"
    - "improving the expected improvement algorithm"
    - "bayesian optimal active search and surveying"

Connected through hyperedge HE-3 (induced by source paper "deep bo for hyperparameter optimization of deep networks"):
  with members:
    - "tapas train less accuracy predictor for architecture search"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "accelerating neural architecture search using performance prediction"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "knowledge matters importance of prior information for optimization"
    - "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-4 (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"
    - "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-5 (induced by source paper "towards assessing the impact of bayesian optimization s own hyperparameters"):
  with members:
    - "auto keras an efficient neural architecture search system"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "learning to optimize"
    - "exploiting compositionality to explore a large space of model structures"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "automatic calibration of dynamic and heterogeneous parameters in agent based model"):
  with members:
    - "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"
    - "an informational approach to the global optimization of expensive to evaluate functions"

Connected through hyperedge HE-8 (induced by source paper "active learning for autonomous intelligent agents exploration curiosity and interaction"):
  with members:
    - "multi class generalized binary search for active inverse reinforcement learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "selecting the state representation in reinforcement learning"
    - "local utility elicitation in gai models"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "safe exploration in markov decision 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.LG(Machine Learning)