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 "better security bug report classification via hyperparameter optimization"):
  Abstract: When security bugs are detected, they should be (a)~discussed privately by security software engineers; and (b)~not mentioned to the general public until security patches are available. Software engineers usually report bugs to bug tracking system, and label them as security bug reports (SBRs) or not-security bug reports (NSBRs), while SBRs have a higher priority to be fixed before exploited by attackers than NSBRs. Yet suspected security bug reports are often publicly disclosed because the mislabelling issues ( i.e., mislabel security bug reports as not-security bug report). The goal of this paper is to aid software developers to better classify bug reports that identify security vulnerabilities as security bug reports through parameter tuning of learners and data pre-processor. Previous work has applied text analytics and machine learning learners to classify which reported bugs are security related. We improve on that work, as shown by our analysis of five open source projects. We apply hyperparameter optimization to (a)~the control parameters of a learner; and (b)~the data pre-processing methods that handle the case where the target class is a small fraction of all the data. We show that optimizing the pre-processor is more useful than optimizing the learners. We also show that improvements gained from our approach can be very large. For example, using the same data sets as recently analyzed by our baseline approach, we show that adjusting the data pre-processing results in improvements to classification recall of 35% to 65% (median to max) with moderate increment of false positive rate.

Includes members:
  Member "bad smells in software analytics papers":
    also connected through other hyperedges:
      (none)
  Member "is better data better than better data miners on the benefits of tuning smote for defect prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to improve ai tools by adding in se knowledge experiments with the timelime defect reduction tool")
  Member "what is wrong with topic modeling and how to fix it using search based software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "hyperparameter optimization for effort estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross dataset design discussion mining")
  Member "less is more minimizing code reorganization using xtree":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "is ai different for se")
  Member "better data is better than better data miners benefits of tuning smote for defect prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluating software defect prediction performance an updated benchmarking study")
  Member "a brief survey of text mining classification clustering and extraction techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "flowsense a natural language interface for visual data exploration within a dataflow system")
  Member "why is differential evolution better than grid search for tuning defect predictors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how to dodge complex software analytics")

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)