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: "self adaptive static analysis"
  Abstract: Static code analysis is a powerful approach to detect quality deficiencies such as performance bottlenecks, safety violations or security vulnerabilities already during a software system's implementation. Yet, as current software systems continue to grow, current static-analysis systems more frequently face the problem of insufficient scalability. We argue that this is mainly due to the fact that current static analyses are implemented fully manually, often in general-purpose programming languages such as Java or C, or in declarative languages such as Datalog. This design choice predefines the way in which the static analysis evaluates, and limits the optimizations and extensions static-analysis designers can apply. #R##N#To boost scalability to a new level, we propose to fuse static-analysis with just-in-time-optimization technology, introducing for the first time static analyses that are managed and inherently self-adaptive. Those analyses automatically adapt themselves to yield a performance/precision tradeoff that is optimal with respect to the analyzed software system and to the analysis itself. #R##N#Self-adaptivity is enabled by the novel idea of designing a dedicated intermediate representation, not for the analyzed program but for the analysis itself. This representation allows for an automatic optimization and adaptation of the analysis code, both ahead-of-time (through static analysis of the static analysis) as well as just-in-time during the analysis' execution, similar to just-in-time compilers.

Connected through hyperedge HE-1 (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"
    - "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"

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)