human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "fast dpp sampling for nystr om with application to kernel methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "submodular batch selection for training deep neural networks")
  Member "parallel predictive entropy search for batch global optimization of expensive objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one shot bayes opt with probabilistic population based training")
  Member "randomized rounding for the largest simplex problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "batch active learning using determinantal point processes")
  Member "parallel gaussian process optimization with upper confidence bound and pure exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one shot bayes opt with probabilistic population based training")
  Member "auto weka combined selection and hyperparameter optimization of classification algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")
  Member "information theoretic regret bounds for gaussian process optimization in the bandit setting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "exponential inapproximability of selecting a maximum volume sub matrix":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "batch active learning using determinantal point processes")

Question: Please tell me which class the source paper 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)