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: "clinically verified pre screening for cancer using web search queries initial results"
  Abstract: Search engine queries have been demonstrated to be a useful signal for screening people for different cancer types. Past work focused on a biased population which indicated that they were suffering from the condition, or else inferred which people had the condition of interest using their queries. Here we used a combination of an online advertising campaign and a clinically verified questionnaire to identify at-risk people, and correlated their past queries with these risk scores. People who suspected they were suffering from lung, breast, or colon cancer were recruited through ads shown on the Bing search engine to complete a clinically verified risk questionnaire. Of those, 201 people agreed to participate in the research and their past queries could be obtained. An automated classifier to predict their risk score based on past queries reached an Area Under the ROC (AUC) of 0.64 for all cancers, and 0.76 for colon cancer. These results demonstrate the utility of search engine queries to screen for cancer and are the represent the first step in utilizing advertising systems to screen for cancer and detect it earlier than has been previously possible.

Connected through hyperedge HE-1 (induced by source paper "algorithmic rewriting of health related ads to improve their performance"):
  with members:
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "detecting parkinson s disease from interactions with a search engine is expert knowledge sufficient"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "computer keyboard interaction as an indicator of early parkinson s disease"

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)