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: "batched gaussian process bandit optimization via determinantal point processes"
  Abstract: Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy black box functions. One example in machine learning is hyper-parameter optimization where each evaluation of the target function requires training a model which may involve days or even weeks of computation. Most methods for this so-called "Bayesian optimization" only allow sequential exploration of the parameter space. However, it is often desirable to propose batches or sets of parameter values to explore simultaneously, especially when there are large parallel processing facilities at our disposal. Batch methods require modeling the interaction between the different evaluations in the batch, which can be expensive in complex scenarios. In this paper, we propose a new approach for parallelizing Bayesian optimization by modeling the diversity of a batch via Determinantal point processes (DPPs) whose kernels are learned automatically. This allows us to generalize a previous result as well as prove better regret bounds based on DPP sampling. Our experiments on a variety of synthetic and real-world robotics and hyper-parameter optimization tasks indicate that our DPP-based methods, especially those based on DPP sampling, outperform state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "regret bounds for batched bandits"):
  with members:
    - "batch policy learning under constraints"
    - "adaptivity in adaptive submodularity"
    - "introduction to multi armed bandits"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "stochastic submodular cover with limited adaptivity"
    - "delay and cooperation in nonstochastic bandits"
    - "online learning with switching costs and other adaptive adversaries"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"

Connected through hyperedge HE-2 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-3 (induced by source paper "bayesian optimization for iterative learning"):
  with members:
    - "practical multi fidelity bayesian optimization for hyperparameter tuning"
    - "bayesian optimization in alphago"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "accelerating neural architecture search using performance prediction"
    - "deep reinforcement learning that matters"
    - "deep bayesian active learning with image data"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"

Connected through hyperedge HE-4 (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"
    - "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"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-5 (induced by source paper "sampling acquisition functions for batch bayesian optimization"):
  with members:
    - "on kernelized multi armed bandits"
    - "xgboost a scalable tree boosting system"
    - "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"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-6 (induced by source paper "on batch bayesian optimization"):
  with members:
    - "on kernelized multi armed bandits"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "hybrid batch bayesian optimization"

Connected through hyperedge HE-7 (induced by source paper "automl from service provider s perspective multi device multi tenant model selection with gp ei"):
  with members:
    - "introduction to online convex optimization"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "regret bounds for deterministic gaussian process bandits"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "autoprognosis automated clinical prognostic modeling via bayesian optimization with structured kernel learning"):
  with members:
    - "personalized donor recipient matching for organ transplantation"
    - "xgboost a scalable tree boosting system"
    - "scikit learn machine learning in python"

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)