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: "screening for cancer using a learning internet advertising system"
  Abstract: Studies have shown that the traces people leave when browsing the internet may indicate the onset of diseases such as cancer. Here we show that the adaptive engines of advertising systems in conjunction with clinically verified questionnaires can be used to identify people who are suspected of having one of three types of solid tumor cancers. #R##N#In the first study, 308 people were recruited through ads shown on the Bing search engine to complete a clinically verified risk questionnaire. A classifier trained to predict questionnaire response using only past queries on Bing reached an Area Under the Curve of 0.64 for all three cancer types, verifying that past searches could be used to identify people with suspected cancer. #R##N#The second study was conducted using the Google ads system in the same configuration as in the first study. However, in this study the ads system was set to automatically learn to identify people with suspected cancer. A total of 70,586 people were shown the ads, and 6,484 clicked and were referred to complete the clinical questionnaires. People from countries with higher Internet access and lower life expectancy tended to click more on the ads. Over time the advertisement system learned to identify people who were likely to have symptoms consistent with suspected cancer, such that the percentage of people completing the questionnaires and found to have suspected cancer reached approximately 11\% at the end of the experiment. #R##N#These results demonstrate the utility of using search engine queries to screen for possible cancer and the application of modern advertising systems to help identify people who are likely suffering from serious medical conditions. This is especially true in countries where medical services are less 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.CY(Computers and Society)