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: "factors and actors leading to the adoption of a javascript framework"
  Abstract: The increasing popularity of JavaScript has led to a variety of JavaScript frameworks that aim to help developers to address programming tasks. However, the number of JavaScript frameworks has risen rapidly to thousands of versions. It is challenging for practitioners to identify the frameworks that best fit their needs and to develop new ones which fit such needs. Furthermore, there is a lack of knowledge regarding what drives developers towards the choice. This paper explores the factors and actors that lead to the choice of a JavaScript framework. We conducted a qualitative interpretive study of semi-structured interviews. We interviewed 18 decision makers regarding the JavaScript framework selection, up to reaching theoretical saturation. Through coding the interview responses, we offer a model of desirable JavaScript framework adoption factors. The factors are grouped into categories that are derived via the Unified Theory of Acceptance and Use of Technology. The factors are performance expectancy (performance, size), effort expectancy (automatization, learnability, complexity, understandability), social influence (competitor analysis, collegial advice, community size, community responsiveness), facilitating conditions (suitability, updates, modularity, isolation, extensibility), and price value. A combination of four actors, which are customer, developer, team, and team leader, leads to the choice. Our model contributes to the body of knowledge related to the adoption of technology by software engineers. As a practical implication, our model is useful for decision makers when evaluating JavaScript frameworks, as well as for developers for producing desirable frameworks.

Connected through hyperedge HE-1 (induced by source paper "a demand side viewpoint to software vulnerabilities in wordpress plugins"):
  with members:
    - "analyzing the perceived severity of cybersecurity threats reported on social media"
    - "security issues in language based sofware ecosystems"
    - "an empirical analysis of vulnerabilities in python packages for web applications"
    - "how php releases are adopted in the wild"

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)