human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting antimicrobial drug consumption using web search data"):
  Abstract: Consumption of antimicrobial drugs, such as antibiotics, is linked with antimicrobial resistance. Surveillance of antimicrobial drug consumption is therefore an important element in dealing with antimicrobial resistance. Many countries lack sufficient surveillance systems. Usage of web mined data therefore has the potential to improve current surveillance methods. To this end, we study how well antimicrobial drug consumption can be predicted based on web search queries, compared to historical purchase data of antimicrobial drugs. We present two prediction models (linear Elastic Net, and non-linear Gaussian Processes), which we train and evaluate on almost 6 years of weekly antimicrobial drug consumption data from Denmark and web search data from Google Health Trends. We present a novel method of selecting web search queries by considering diseases and drugs linked to antimicrobials, as well as professional and layman descriptions of antimicrobial drugs, all of which we mine from the open web. We find that predictions based on web search data are marginally more erroneous but overall on a par with predictions based on purchases of antimicrobial drugs. This marginal difference corresponds to ∠1% point mean absolute error in weekly usage. Best predictions are reported when combining both web search and purchase data. This study contributes a novel alternative solution to the real-life problem of predicting (and hence monitoring) antimicrobial drug consumption, which is particularly valuable in countries/states lacking centralised and timely surveillance systems.

Includes members:
  Member "seasonal web search query selection for influenza like illness ili estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "web data mining for public health purposes")
  Member "a study of metrics of distance and correlation between ranked lists for compositionality detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "web data mining for public health purposes")
  Member "time series adaptive estimation of vaccination uptake using web search queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "web data mining for public health purposes")
  Member "ensemble learned vaccination uptake prediction using web search queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "web data mining for public health purposes")
  Member "from cookies to cooks insights on dietary patterns via analysis of web usage logs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale and high resolution analysis of food purchases and health outcomes")
  Member "findzebra a search engine for rare diseases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dependencies formalising semantic catenae for information retrieval")

Question: Please tell me which class the source paper 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)