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: "search based software engineering for self adaptive systems one survey five disappointments and six opportunities"
  Abstract: Search-Based Software Engineering (SBSE) is a promising paradigm that exploits computational search to optimize different processes when engineering complex software systems. Self-adaptive system (SAS) is one category of such complex systems that permits to optimize different functional and non-functional objectives/criteria under changing environment (e.g., requirements and workload), which involves problems that are subject to search. In this regard, over years, there have been a considerable amount of work that investigates SBSE for SASs. In this paper, we provide the first systematic and comprehensive survey exclusively on SBSE for SASs, covering 3,740 papers in 27 venues from 7 repositories, which eventually leads to several key statistics from the most notable 73 primary studies in this particular field of research. Our results, surprisingly, have revealed five disappointed issues that are of utmost importance, but have been overwhelmingly ignored in existing studies. We provide evidences to justify our arguments against the disappointments and highlight six emergent, but currently under-explored opportunities for future work on SBSE for SASs. By mitigating the disappointed issues revealed in this work, together with the highlighted opportunities, we hope to be able to excite a much more significant growth on this particular research direction.

Connected through hyperedge HE-1 (induced by source paper "how to evaluate solutions in pareto based search based software engineering a critical review and methodological guidance"):
  with members:
    - "understanding the automated parameter optimization on transfer learning for cpdp an empirical study"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "how to read many objective solution sets in parallel coordinates"
    - "sampling as a baseline optimizer for search based software engineering"
    - "elegant object oriented software design via interactive evolutionary computation"

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)