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 "understanding the automated parameter optimization on transfer learning for cpdp an empirical study"):
  Abstract: Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows data/knowledge from other projects to facilitate the model building at the current project, namely cross-project defect prediction (CPDP), is naturally plausible. Most CPDP techniques involve two major steps, i.e., transfer learning and classification, each of which has at least one parameter to be tuned to achieve their optimal performance. This practice fits well with the purpose of automated parameter optimization. However, there is a lack of thorough understanding about what are the impacts of automated parameter optimization on various CPDP techniques. In this paper, we present the first empirical study that looks into such impacts on 62 CPDP techniques, 13 of which are chosen from the existing CPDP literature while the other 49 ones have not been explored before. We build defect prediction models over 20 real-world software projects that are of different scales and characteristics. Our findings demonstrate that: (1) Automated parameter optimization substantially improves the defect prediction performance of 77\% CPDP techniques with a manageable computational cost. Thus more efforts on this aspect are required in future CPDP studies. (2) Transfer learning is of ultimate importance in CPDP. Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques. This finding provides important insights about the future design of CPDP techniques.

Includes members:
  Member "which surrogate works for empirical performance modelling a case study with differential evolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "surrogate assisted evolutionary algorithm for medium scale expensive multi objective optimisation problems")
  Member "visualisation of pareto front approximation a short survey and empirical comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "surrogate assisted evolutionary algorithm for medium scale expensive multi objective optimisation problems")
  Member "how to dodge complex software analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "is ai different for se")
  Member "transfer learning oriented class imbalance learning for cross project defect prediction":
    also connected through other hyperedges:
      (none)
  Member "the impact of automated parameter optimization on defect prediction models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "puminer mining security posts from developer question and answer websites with pu learning")
  Member "femosaa feature guided and knee driven multi objective optimization for self adaptive software":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how to evaluate solutions in pareto based search based software engineering a critical review and methodological guidance")
  Member "a systematic mapping study on cross project defect prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the costs and profit of software defect prediction")
  Member "evolutionary many objective optimization based on adversarial decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "routing led placement of vnfs in arbitrary networks")

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.NE(Neural and Evolutionary Computing)