human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "user interface design smell automatic detection and refactoring of blob listeners"
  Abstract: Context. User Interfaces (UIs) intensively rely on event-driven programming: widgets send UI events, which capture users' interactions, to dedicated objects called controllers. Controllers use several UI listeners that handle these events to produce UI commands. Objective. First, we reveal the presence of design smells in the code that describes and controls UIs. Second, we demonstrate that specific code analyses are necessary to analyze and refactor UI code, because of its coupling with the rest of the code. Method. We conducted an empirical study on four large Java Swing and SWT open-source software systems: Eclipse, JabRef, ArgouML, and FreeCol. We study to what extent the number of UI commands that a UI listener can produce has an impact on the change-and fault-proneness of the UI listener code. We develop a static code analysis for detecting UI commands in the code. Results. We identify a new type of design smell, called Blob listener that characterizes UI listeners that can produce more than two UI commands. We propose a systematic static code analysis procedure that searches for Blob listener that we implement in InspectorGuidget. We conducted experiments on the four software systems for which we manually identified 53 instances of Blob listener. InspectorGuidget successfully detected 52 Blob listeners out of 53. The results exhibit a precision of 81.25 % and a recall of 98.11 %. We then developed a semi-automatically and behavior-preserving refactoring process to remove Blob listeners. 49.06 % of the 53 Blob listeners were automatically refactored. Patches for JabRef, and FreeCol have been accepted and merged. Discussions with developers of the four software systems assess the relevance of the Blob listener. Conclusion. This work shows that UI code also suffers from design smells that have to be identified and characterized. We argue that studies have to be conducted to find other UI design smells and tools that analyze UI code must be developed.

Question: Please tell me which class the center node 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)