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 "sequential model optimization for software process control"):
  Abstract: Many methods have been proposed to estimate how much effort is required to build and maintain software. Much of that research assumes a "classic" waterfall-based approach rather than contemporary agile projects. Also, much of that work tries to recommend a single method-- an approach that makes the dubious assumption that one method can handle the diversity of software project data. #R##N#To address these drawbacks, we apply a configuration technique called "ROME" (Rapid Optimizing Methods for Estimation), which uses sequential model-based optimization to find what combination of techniques works best for a particular data set. In this paper, we test this method using data from 1161 classic waterfall projects and 446 contemporary agile projects (from Github). #R##N#We find that ROME achieves better performance (in terms of magnitude of relative error and standardized accuracy) than existing state-of-the-art methods for both classic and contemporary problems. From this work, we conclude that it is not best to recommend method for estimation. Rather, it is better to search through a wide range of different methods to find what works best for local data. #R##N#To the best of our knowledge, this is the largest effort estimation experiment yet attempted and the only one to test its methods on classic and contemporary projects.

Includes members:
  Member "finding faster configurations using flash":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning software configuration spaces a systematic literature review")
  Member "data driven search based software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster creation of smaller test suites with snap")
  Member "warmstarting of model based algorithm configuration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "what is the connection between issues bugs and enhancements lessons learned from 800 software projects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "security smells in infrastructure as code scripts")
  Member "negative results for software effort estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyperparameter optimization for effort estimation")
  Member "why is differential evolution better than grid search for tuning defect predictors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to dodge complex software analytics")
  Member "tuning for software analytics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how to improve ai tools by adding in se knowledge experiments with the timelime defect reduction tool")

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)