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: "comparing scientific and technological impact of biomedical research"
  Abstract: Abstract   Traditionally, the number of citations that a scholarly paper receives from other papers is used as the proxy of its scientific impact. Yet citations can come from domains outside the scientific community, and one such example is through patented technologies—paper can be cited by patents, achieving technological impact. While the scientific impact of papers has been extensively studied, the technological aspect remains less known in the literature. Here we aim to fill this gap by presenting a comparative study on how 919 thousand biomedical papers are cited by U.S. patents and by other papers over time. We observe a positive correlation between citations from patents and from papers, but there is little overlap between the two domains in either the most cited papers, or papers with the most delayed recognition. We also find that the two types of citations exhibit distinct temporal variations, with patent citations lagging behind paper citations for a median of 6 years for the majority of papers. Our work contributes to the understanding of the technological impact of papers.

Connected through hyperedge HE-1 (induced by source paper "paper patent citation linkages as early signs for predicting delayed recognized knowledge macro and micro evidence"):
  with members:
    - "patent citations to scientific papers as early signs for predicting delayed recognition of scientific discoveries a comparative study with instant recognition"
    - "smart girls versus sleeping beauties in the sciences the identification of instant and delayed recognition by using the citation angle"

Connected through hyperedge HE-2 (induced by source paper "an analysis of the evolution of science technology linkage in biomedicine"):
  with members:
    - "identifying translational science through embeddings of controlled vocabularies"
    - "a triple helix model of medical innovation supply demand and technological capabilities in terms of medical subject headings"

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.DL(Digital Libraries)