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: "grand challenges in measuring and characterizing scholarly impact"
  Abstract: The constantly growing body of scholarly knowledge of science, technology, and humanities is an asset of the mankind. While new discoveries expand the existing knowledge, they may simultaneously render some of it obsolete. It is crucial for scientists and other stakeholders to keep their knowledge up to date. Policy makers, decision makers, and the general public also need an efficient communication of scientific knowledge. Several grand challenges concerning the creation, adaptation, and diffusion of scholarly knowledge, and advance quantitative and qualitative approaches to the study of scholarly knowledge are identified.

Connected through hyperedge HE-1 (induced by source paper "a scalable and adaptive method for finding semantically equivalent cue words of uncertainty"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "deep learning in neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "cited references and medical subject headings mesh as two different knowledge representations clustering and mappings at the paper level"):
  with members:
    - "strategic intelligence on emerging technologies scientometric overlay mapping"
    - "a triple helix model of medical innovation supply demand and technological capabilities in terms of medical subject headings"
    - "introducing citedreferencesexplorer crexplorer a program for reference publication year spectroscopy with cited references standardization"
    - "can hot spots in the sciences be mapped using the dynamics of aggregated journal journal citation relations"
    - "citation analysis with medical subject headings mesh using the web of knowledge a new routine"
    - "a new methodology for constructing a publication level classification system of science"
    - "some modifications to the snip journal impact indicator"
    - "measuring contextual citation impact of scientific journals"

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)