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: "using bad learners to find good configurations"
  Abstract: Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, building an accurate performance model can be very expensive (and is often infeasible in practice). The central insight of this paper is that exact performance values (e.g., the response time of a software system) are not required to rank configurations and to identify the optimal one. As shown by our experiments, performance models that are cheap to learn but inaccurate (with respect to the difference between actual and predicted performance) can still be used rank configurations and hence find the optimal configuration. This novel rank-based approach allows us to significantly reduce the cost (in terms of number of measurements of sample configuration) as well as the time required to build performance models. We evaluate our approach with 21 scenarios based on 9 software systems and demonstrate that our approach is beneficial in 16 scenarios; for the remaining 5 scenarios, an accurate model can be built by using very few samples anyway, without the need for a rank-based approach.

Connected through hyperedge HE-1 (induced by source paper "towards quality assurance of software product lines with adversarial configurations"):
  with members:
    - "learning software configuration spaces a systematic literature review"
    - "test them all is it worth it assessing configuration sampling on the jhipster web development stack"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "robust physical world attacks on deep learning models"

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"
    - "on the relation of external and internal feature interactions a case study"
    - "faster discovery of faster system configurations with spectral learning"

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"
    - "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-4 (induced by source paper "predicting performance of software configurations there is no silver bullet"):
  with members:
    - "finding faster configurations using flash"
    - "easy over hard a case study on deep learning"
    - "faster discovery of faster system configurations with spectral 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-5 (induced by source paper "classytune a performance auto tuner for systems in the cloud"):
  with members:
    - "large scale evolution of image classifiers"
    - "acts in need automatic configuration tuning with scalability guarantees"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "an overview on clustering methods"
    - "learning to order things"
    - "the asilomar report on database research"

Connected through hyperedge HE-6 (induced by source paper "better software analytics via duo data mining algorithms using used by optimizers"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "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"
    - "data driven search based software engineering"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"
    - "an improved generic bet and run strategy for speeding up stochastic local search"

Connected through hyperedge HE-7 (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"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"

Connected through hyperedge HE-8 (induced by source paper "micky a cheaper alternative for selecting cloud instances"):
  with members:
    - "finding faster configurations using flash"
    - "scout an experienced guide to find the best cloud configuration"
    - "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.SE(Software Engineering)