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: "faster discovery of faster system configurations with spectral learning"
  Abstract: Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors - less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2 to 10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.

Connected through hyperedge HE-1 (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-2 (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"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

Connected through hyperedge HE-3 (induced by source paper "predicting performance of software configurations there is no silver bullet"):
  with members:
    - "finding faster configurations using flash"
    - "using bad learners to find good configurations"
    - "easy over hard a case study on deep learning"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "tuning for software analytics"
    - "distribution aware sampling and weighted model counting for sat"

Connected through hyperedge HE-4 (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"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

Connected through hyperedge HE-5 (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-6 (induced by source paper "auto tuning distributed stream processing systems using reinforcement learning"):
  with members:
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement learning for multi resource multi machine job scheduling"
    - "tensorflow a system for large scale machine learning"
    - "drs dynamic resource scheduling for real time analytics over fast streams"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-7 (induced by source paper "riot a novel stochastic method for rapidly configuring cloud based workflows"):
  with members:
    - "transfer learning for improving model predictions in highly configurable software"
    - "using bad learners to find good configurations"
    - "beyond evolutionary algorithms for search based software engineering"
    - "sampling as a baseline optimizer for search based software engineering"
    - "deep api learning"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"

Connected through hyperedge HE-8 (induced by source paper "using bad learners to find good configurations"):
  with members:
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

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.SE(Software Engineering)