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 "is better data better than better data miners on the benefits of tuning smote for defect prediction"):
  Abstract: We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-performance criteria while (b) fixing the weaker regions of the training data (using SMOTUNED, which is an auto-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions when applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for defects. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique.   In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.

Includes members:
  Member "what is wrong with topic modeling and how to fix it using search based software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "less is more minimizing code reorganization using xtree":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "is ai different for se")
  Member "better predictors for issue lifetime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "is ai different for se")
  Member "revisiting unsupervised learning for defect prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster creation of smaller test suites with snap")
  Member "easy over hard a case study on deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "tuning for software analytics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to improve ai tools by adding in se knowledge experiments with the timelime defect reduction tool")
  Member "synthetic minority over sampling technique smote for predicting software build outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for malicious flow detection")
  Member "smote synthetic minority over sampling technique":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")

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)