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: "test them all is it worth it assessing configuration sampling on the jhipster web development stack"
  Abstract: Many approaches for testing configurable software systems start from the same assumption: it is impossible to test all configurations. This motivated the definition of variability-aware abstractions and sampling techniques to cope with large configuration spaces. Yet, there is no theoretical barrier that prevents the exhaustive testing of all configurations by simply enumerating them if the effort required to do so remains acceptable. Not only this: we believe there is a lot to be learned by systematically and exhaustively testing a configurable system. In this case study, we report on the first ever endeavour to test all possible configurations of the industry-strength, open source configurable software system JHipster, a popular code generator for web applications. We built a testing scaffold for the 26,000+ configurations of JHipster using a cluster of 80 machines during 4 nights for a total of 4,376 hours (182 days) CPU time. We find that 35.70% configurations fail and we identify the feature interactions that cause the errors. We show that sampling strategies (like dissimilarity and 2-wise): (1) are more effective to find faults than the 12 default configurations used in the JHipster continuous integration; (2) can be too costly and exceed the available testing budget. We cross this quantitative analysis with the qualitative assessment of JHipster’s lead developers.

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"
    - "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"
    - "using bad learners to find good configurations"

Connected through hyperedge HE-2 (induced by source paper "configcrusher white box performance analysis for configurable systems"):
  with members:
    - "igen dynamic interaction inference for configurable software"
    - "do android taint analysis tools keep their promises"
    - "self adaptive static analysis"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "precise dynamic information flow for database backed applications"

Connected through hyperedge HE-3 (induced by source paper "mining container image repositories for software configuration and beyond"):
  with members:
    - "an hci view of configuration problems"

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)