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: "seasonal web search query selection for influenza like illness ili estimation"
  Abstract: Influenza-like illness (ILI) estimation from web search data is an important web analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating current ILI activity. It has been noted that since influenza is seasonal, this approach can lead to spurious correlations with features/queries that also exhibit seasonality, but have no relationship with ILI. Spurious correlations can, in turn, degrade performance. To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal. Experimental results show that re-ranking queries obtained by Google Correlate based on their correlation with the residual strongly favours ILI-related queries.

Connected through hyperedge HE-1 (induced by source paper "web data mining for public health purposes"):
  with members:
    - "a study of metrics of distance and correlation between ranked lists for compositionality detection"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "ensemble learned vaccination uptake prediction using web search queries"

Connected through hyperedge HE-2 (induced by source paper "predicting antimicrobial drug consumption using web search data"):
  with members:
    - "a study of metrics of distance and correlation between ranked lists for compositionality detection"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "ensemble learned vaccination uptake prediction using web search queries"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"
    - "findzebra a search engine for rare diseases"

Connected through hyperedge HE-3 (induced by source paper "smart city analytics ensemble learned prediction of citizen home care"):
  with members:
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "ensemble learned vaccination uptake prediction using web search queries"

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.IR(Information Retrieval)