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: "patent citations to scientific papers as early signs for predicting delayed recognition of scientific discoveries a comparative study with instant recognition"
  Abstract: In this study, we investigate the extent to that patent citations to papers can serve as early signs for predicting delayed recognition using a comparative study with a control group, i.e., instant recognition papers. We identify the two opposite groups of papers by the Bcp measure, a parameter-free index for identifying papers which were recognized with delay (also called "sleeping beauties" in science). Combined with a typical case study, it appears that papers with delayed recognition show a stronger and longer technical impact than instant recognition papers. We provide indication that in the more recent years papers with delayed recognition are awakened more often and more earlier by a patent rather than by a scientific paper (also called "prince"). We also found that patent citations seem to play an important role to avoid instant recognition papers to level off or to become a so called "flash in the pan". It also appears that the sleeping beauties may firstly encounter negative citations and then patent citations and finally get widely recognized. In contrast to the two focus fields (biology and chemistry) for instant recognition papers, delayed recognition papers are rather evenly distributed in biology, chemistry, psychology, geology, materials science, and physics. We discovered several pairs of "science sleeping"-"technology inducing", such as biology-biotechnology/pharmaceuticals, chemistry-chemical engineering, psychology-computer/control technology, and physics-computer technology. We propose in further research to discover the potential ahead of time and transformative research by using citation delay analysis, patent & NPL analysis, and citation context analysis.

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:
    - "comparing scientific and technological impact of biomedical research"
    - "smart girls versus sleeping beauties in the sciences the identification of instant and delayed recognition by using the citation angle"

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)