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: "time series adaptive estimation of vaccination uptake using web search queries"
  Abstract: Estimating vaccination uptake is an integral part of ensuring public health. It was recently shown that vaccination uptake can be estimated automatically from web data, instead of slowly collected clinical records or population surveys. All prior work in this area assumes that features of vaccination uptake collected from the web are temporally regular. We present the first ever method to remove this assumption from vaccination uptake estimation: our method dynamically adapts to temporal fluctuations in time series web data used to estimate vaccination uptake. We show our method to outperform the state of the art compared to competitive baselines that use not only web data but also curated clinical data. This performance improvement is more pronounced for vaccines whose uptake has been irregular due to negative media attention (HPV-1 and HPV-2), problems in vaccine supply (DiTeKiPol), and targeted at children of 12 years old (whose vaccination is more irregular compared to younger children).

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"
    - "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:
    - "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"
    - "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 "seasonal web search query selection for influenza like illness ili estimation"):
  with members:
    - "ensemble learned vaccination uptake prediction 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"
    - "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"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

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"
    - "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)