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: "scout an experienced guide to find the best cloud configuration"
  Abstract: Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases application performance and increases running cost significantly. While Bayesian Optimization is effective and applicable to any workloads, it is fragile because performance and workload are hard to model (to predict). #R##N#In this paper, we propose a novel method, SCOUT. The central insight of SCOUT is that using prior measurements, even those for different workloads, improves search performance and reduces search cost. At its core, SCOUT extracts search hints (inference of resource requirements) from low-level performance metrics. Such hints enable SCOUT to navigate through the search space more efficiently---only spotlight region will be searched. #R##N#We evaluate SCOUT with 107 workloads on Apache Hadoop and Spark. The experimental results demonstrate that our approach finds better cloud configurations with a lower search cost than state of the art methods. #R##N#Based on this work, we conclude that (i) low-level performance information is necessary for finding the right cloud configuration in an effective, efficient and reliable way, and (ii) a search method can be guided by historical data, thereby reducing cost and improving performance.

Connected through hyperedge HE-1 (induced by source paper "whence to learn transferring knowledge in configurable systems using beetle"):
  with members:
    - "finding faster configurations using flash"
    - "micky a cheaper alternative for selecting cloud instances"
    - "transfer learning for improving model predictions in highly configurable software"
    - "bellwethers a baseline method for transfer learning"
    - "using bad learners to find good configurations"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "faster discovery of faster system configurations with spectral learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

Connected through hyperedge HE-2 (induced by source paper "lynceus tuning and provisioning data analytic jobs on a budget"):
  with members:
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "adam a method for stochastic optimization"
    - "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 "transfer learning with bellwethers to find good configurations"):
  with members:
    - "finding faster configurations using flash"
    - "low level augmented bayesian optimization for finding the best cloud vm"
    - "bellwethers a baseline method for transfer learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"
    - "learning transferable features with deep adaptation networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "micky a cheaper alternative for selecting cloud instances"):
  with members:
    - "finding faster configurations using flash"
    - "using bad learners to find good configurations"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "cloud migration a case study of migrating an enterprise it system to iaas"
    - "reinforcement learning a survey"

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.DC(Distributed, Parallel, and Cluster Computing)