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: "unreported links between trial registrations and published articles were identified using document similarity measures in a cross sectional analysis of clinicaltrials gov"
  Abstract: Abstract   Objectives  Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between  ClinicalTrials.gov  and PubMed.    Study Design and Setting  We extracted terms and concepts from a data set of 72,469  ClinicalTrials.gov  registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order.    Results  The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1–21) for reported links and 3 (IQR 1–19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1–20) for reported links and 2 (IQR 1–12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links.    Conclusion  Leveraging the growth in the corpus of reported links between  ClinicalTrials.gov  and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.

Connected through hyperedge HE-1 (induced by source paper "recommending research articles to consumers of online vaccination information"):
  with members:
    - "automatically applying a credibility appraisal tool to track vaccination related communications shared on social media"
    - "deep contextualized word representations"
    - "universal language model fine tuning for text classification"
    - "distributed representations of words and phrases and their compositionality"

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)