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: "ensemble learned vaccination uptake prediction using web search queries"
  Abstract: We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official vaccine records show that our method predicts vaccination uptake effectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the first study to predict vaccination uptake using web data (with and without clinical data).

Connected through hyperedge HE-1 (induced by source paper "web data mining for public health purposes"):
  with members:
    - "seasonal web search query selection for influenza like illness ili estimation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "predicting antimicrobial drug consumption using web search data"):
  with members:
    - "seasonal web search query selection for influenza like illness ili estimation"
    - "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"
    - "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 "seasonal web search query selection for influenza like illness ili estimation"):
  with members:
    - "time series adaptive estimation of vaccination uptake using web search queries"

Connected through hyperedge HE-4 (induced by source paper "dependencies formalising semantic catenae for information retrieval"):
  with members:
    - "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval"
    - "preliminary experiments using subjective logic for the polyrepresentation of information needs"
    - "rhetorical relations for information retrieval"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "part of speech based term weighting for information retrieval"
    - "learning to generate compositional color descriptions"
    - "adaptive distributional extensions to dfr ranking"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"

Connected through hyperedge HE-5 (induced by source paper "smart city analytics ensemble learned prediction of citizen home care"):
  with members:
    - "seasonal web search query selection for influenza like illness ili estimation"
    - "time series adaptive estimation of vaccination uptake 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)