human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on using retrained and incremental machine learning for modeling performance of adaptable software an empirical comparison"):
  Abstract: Given the ever-increasing complexity of adaptable software systems and their commonly hidden internal information (e.g., software runs in the public cloud), machine learning based performance modeling has gained momentum for evaluating, understanding and predicting software performance, which facilitates better informed self-adaptations. As performance data accumulates during the run of the software, updating the performance models becomes necessary. To this end, there are two conventional modeling methods: the retrained modeling that always discard the old model and retrain a new one using all available data; or the incremental modeling that retains the existing model and tunes it using one newly arrival data sample. Generally, literature on machine learning based performance modeling for adaptable software chooses either of those methods according to a general belief, but they provide insufficient evidences or references to justify their choice. This paper is the first to report on a comprehensive empirical study that examines both modeling methods under distinct domains of adaptable software, 5 performance indicators, 8 learning algorithms and settings, covering a total of 1,360 different conditions. Our findings challenge the general belief, which is shown to be only partially correct, and reveal some of the important, statistically significant factors that are often overlooked in existing work, providing evidence-based insights on the choice.

Includes members:
  Member "a survey and taxonomy of self aware and self adaptive cloud autoscaling systems":
    also connected through other hyperedges:
      (none)
  Member "femosaa feature guided and knee driven multi objective optimization for self adaptive software":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding the automated parameter optimization on transfer learning for cpdp an empirical study")
  Member "self adaptive trade off decision making for autoscaling cloud based services":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how to evaluate solutions in pareto based search based software engineering a critical review and methodological guidance")
  Member "transfer learning for improving model predictions in highly configurable software":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning software configuration spaces a systematic literature review")
  Member "daleel simplifying cloud instance selection using machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")

Question: Please tell me which class the source paper 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)