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 "patent citation spectroscopy pcs algorithmic retrieval of landmark patents"):
  Abstract: One essential component in the construction of patent landscapes in biomedical research and development (R&D) is identifying the most seminal patents. Hitherto, the identification of seminal patents required subject matter experts within biomedical areas. In this brief communication, we report an analytical method and tool, Patent Citation Spectroscopy (PCS), for rapidly identifying landmark patents in user-specified areas of biomedical innovation. PCS mines the cited references within large sets of patents and provides an estimate of the most historically impactful prior work. The efficacy of PCS is shown in two case studies of biomedical innovation with clinical relevance: (1) RNA interference and (2) cholesterol. PCS mined and analyzed 4,065 cited references related to patents on RNA interference and correctly identified the foundational patent of this technology, as independently reported by subject matter experts on RNAi intellectual property. Secondly, PCS was applied to a broad set of patents dealing with cholesterol - a case study chosen to reflect a more general, as opposed to expert, patent search query. PCS mined through 11,326 cited references and identified the seminal patent as that for Lipitor, the groundbreaking medication for treating high cholesterol as well as the pair of patents underlying Repatha. These cases suggest that PCS provides a useful method for identifying seminal patents in areas of biomedical innovation and therapeutics. The interactive tool is free-to-use at: www.leydesdorff.net/pcs/.

Includes members:
  Member "strategic intelligence on emerging technologies scientometric overlay mapping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data mining the foundational patents of photovoltaic materials an application of patent citation spectroscopy")
  Member "citation algorithms for identifying research milestones driving biomedical innovation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "early identification of important patents design and validation of citation network metrics")
  Member "citations indicators of quality the impact fallacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications")
  Member "detecting the historical roots of tribology research a bibliometric analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identifying single influential publications in a research field new analysis opportunities of the crexplorer")
  Member "detecting the historical roots of research fields by reference publication year spectroscopy rpys":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how to identify the roots of broad research topics and fields the introduction of rpys sampling using the example of climate change research")
  Member "referenced publication years spectroscopy applied to imetrics scientometrics journal of informetrics and a relevant subset of jasist":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to identify the roots of broad research topics and fields the introduction of rpys sampling using the example of climate change research")

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.DL(Digital Libraries)