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: "an uncertainty aware approach to optimal configuration of stream processing systems"
  Abstract: Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.

Connected through hyperedge HE-1 (induced by source paper "black or white how to develop an autotuner for memory based analytics extended version"):
  with members:
    - "neo a learned query optimizer"
    - "learning based automatic parameter tuning for big data analytics frameworks"
    - "don t cry over spilled records memory elasticity of data parallel applications and its application to cluster scheduling"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "tempo robust and self tuning resource management in multi tenant parallel databases"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning software configuration spaces a systematic literature review"):
  with members:
    - "automated search for configurations of deep neural network architectures"
    - "machine learning meets quantitative planning enabling self adaptation in autonomous robots"
    - "finding faster configurations using flash"
    - "towards adversarial configurations for software product lines"
    - "an efficient method for uncertainty propagation in robust software performance estimation"
    - "transfer learning for improving model predictions in highly configurable software"
    - "using bad learners to find good configurations"
    - "on the relation of external and internal feature interactions a case study"

Connected through hyperedge HE-3 (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"
    - "scout an experienced guide to find the best cloud configuration"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "configcrusher white box performance analysis for configurable systems"):
  with members:
    - "igen dynamic interaction inference for configurable software"
    - "test them all is it worth it assessing configuration sampling on the jhipster web development stack"
    - "do android taint analysis tools keep their promises"
    - "self adaptive static analysis"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "precise dynamic information flow for database backed applications"

Connected through hyperedge HE-5 (induced by source paper "machine learning meets quantitative planning enabling self adaptation in autonomous robots"):
  with members:
    - "a learning approach to enhance assurances for real time self adaptive systems"
    - "transfer learning for improving model predictions in highly configurable software"
    - "faster discovery of faster system configurations with spectral learning"

Connected through hyperedge HE-6 (induced by source paper "data driven search based software engineering"):
  with members:
    - "finding faster configurations using flash"
    - "the impact of automated parameter optimization on defect prediction models"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "finding better active learners for faster literature reviews"
    - "transfer learning for improving model predictions in highly configurable software"
    - "short er reasoning about larger requirements models"
    - "using bad learners to find good configurations"

Connected through hyperedge HE-7 (induced by source paper "transfer learning with bellwethers to find good configurations"):
  with members:
    - "finding faster configurations using flash"
    - "scout an experienced guide to find the best cloud configuration"
    - "low level augmented bayesian optimization for finding the best cloud vm"
    - "bellwethers a baseline method for transfer learning"
    - "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-8 (induced by source paper "finding faster configurations using flash"):
  with members:
    - "beyond evolutionary algorithms for search based software engineering"
    - "easy over hard a case study on deep learning"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)