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: "a comparison of 10 sampling algorithms for configurable systems"
  Abstract: Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.

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"
    - "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 "automated search for configurations of deep neural network architectures"):
  with members:
    - "fundamentals of recurrent neural network rnn and long short term memory lstm network"
    - "finding faster configurations using flash"
    - "efficient neural architecture search via parameter sharing"
    - "searching for test data with feature diversity"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "designing neural network architectures using reinforcement learning"

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"
    - "using bad learners to find good configurations"
    - "easy over hard a case study on deep learning"
    - "faster discovery of faster system configurations with spectral learning"
    - "tuning for software analytics"
    - "distribution aware sampling and weighted model counting for sat"

Connected through hyperedge HE-5 (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"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "precise dynamic information flow for database backed applications"

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)