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: "deriving a usage independent software quality metric"
  Abstract: Context:The extent of post-release use of software affects the number of faults, thus biasing quality metrics and adversely affecting associated decisions. The proprietary nature of usage data limited deeper exploration of this subject in the past. Objective: To determine how software faults and software use are related and how an accurate quality measure can be designed. Method: New users, usage intensity, usage frequency, exceptions, and release date and duration measured for complex proprietary mobile applications for Android and iOS. Utilized Bayesian Network and Random Forest models to explain the interrelationships and to derive the usage independent release quality measure. Investigated the interrelationship among various code complexity measures, usage (downloads), and number of issues for 520 NPM packages and derived a usage-independent quality measure from these analyses, applied it on 4430 popular NPM packages to construct timelines for comparing the perceived quality (issues) and our derived measure of quality for these packages.Results: We found the number of new users to be the primary factor determining the number of exceptions, and found no direct link between the intensity and frequency of software usage and software faults. Release quality expressed as crashes per user was independent of other usage-related predictors, thus serving as a usage independent measure of software quality. Usage also affected quality in NPM, where downloads were strongly associated with numbers of issues, even after taking the other code complexity measures into consideration. Conclusions: We expect our result and our proposed quality measure will help gauge release quality of a software more accurately and inspire further research in this area.

Connected through hyperedge HE-1 (induced by source paper "a dataset and an approach for identity resolution of 38 million author ids extracted from 2b git commits"):
  with members:
    - "patterns of effort contribution and demand and user classification based on participation patterns in npm ecosystem"

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)