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: "developing a cardiovascular disease risk factors annotated corpus of chinese electronic medical records"
  Abstract: Objective The goal of this study was to build a corpus of cardiovascular disease (CVD) risk-factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk-factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk-factors and CVD. Materials and Methods We designed a light-annotation-task to capture CVD-risk-factors with indicators, temporal attributes and assertions explicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Results The outcome of this study was a risk-factor-annotated corpus based on de-identified discharge summaries and progress notes from 600 patients. Built with the help of specialists, this corpus has an inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high reliability. Discussion Our annotations included 12 CVD-risk-factors such as Hypertension and Diabetes. The annotations can be applied as a powerful tool to the management of these chronic diseases and the prediction of CVD. Conclusion Guidelines for capturing CVD-risk-factor annotations from CEMRs were proposed and an annotated corpus was established. The obtained document-level annotations can be applied in future studies to monitor risk-factors and CVD over the long term.

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.CL(Computation and Language)