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 "boosting automatic commit classification into maintenance activities by utilizing source code changes"):
  Abstract: Background: Understanding maintenance activities performed in a source code repository could help practitioners reduce uncertainty and improve cost-effectiveness by planning ahead and pre-allocating resources towards source code maintenance. The research community uses 3 main classification categories for maintenance activities: Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. Previous work in this area has mostly concentrated on evaluating commit classification (into maintenance activities) models in the scope of a single software project. Aims: In this work we seek to design a commit classification model capable of providing high accuracy and Kappa across different projects. In addition, we wish to compare the accuracy and kappa characteristics of classification models that utilize word frequency analysis, source code changes, and combination thereof. Method: We suggest a novel method for automatically classifying commits into maintenance activities by utilizing source code changes (e.g, statement added, method removed, etc.). The results we report are based on studying 11 popular open source projects from various professional domains from which we had manually classified 1151 commits, over 100 from each of the studied projects. Our models were trained using 85% of the dataset, while the remaining 15% were used as a test set. Results: Our method shows a promising accuracy of 76% and Cohen's kappa of 63% (considered "Good" in this context) for the test dataset, an improvement of over 20 percentage points, and a relative boost of ~40% in the context of cross-project classification. Conclusions: We show that by using source code changes in combination with commit message word frequency analysis we are able to considerably boost classification quality in a project agnostic manner.

Includes members:
  Member "using temporal and semantic developer level information to predict maintenance activity profiles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visually exploring software maintenance activities")
  Member "automatically extracting instances of code change patterns with ast analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing the context of bug fixing changes in the openstack cloud computing platform")

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)