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: "characterization description and considerations for the use of funding acknowledgement data in web of science"
  Abstract: Funding acknowledgements found in scientific publications have been used to study the impact of funding on research since the 1970s. However, no broad scale indexation of that paratextual element was done until 2008, when Thomson Reuters' Web of Science started to add funding acknowledgement information to its bibliographic records. As this new information provides a new dimension to bibliometric data that can be systematically exploited, it is important to understand the characteristics of these data and the underlying implications for their use. This paper analyses the presence and distribution of funding acknowledgement data covered in Web of Science. Our results show that prior to 2009 funding acknowledgements coverage is extremely low and therefore not reliable. Since 2008, funding information has been collected mainly for publications indexed in the Science Citation Index Expanded; more recently (2015), inclusion of funding texts for publications indexed in the Social Science Citation Index has been implemented. Arts & Humanities Citation Index content is not indexed for funding acknowledgement data. Moreover, English-language publications are the most reliably covered. Finally, not all types of documents are equally covered for funding information indexation and only articles and reviews show consistent coverage. The characterization of the funding acknowledgement information collected by Thomson Reuters can therefore help understand the possibilities offered by the data but also their limitations.

Connected through hyperedge HE-1 (induced by source paper "china may need to support more small teams in scientific research"):
  with members:
    - "practical method to reclassify web of science articles into unique subject categories and broad disciplines"
    - "the preeminence of ethnic diversity in scientific collaboration"
    - "collaboration diversity and scientific impact"
    - "funding acknowledgment analysis queries and caveats"
    - "the relationship among research productivity research collaboration and their determinants"
    - "understanding the group dynamics and success of teams"
    - "field normalized citation impact indicators and the choice of an appropriate counting method"

Connected through hyperedge HE-2 (induced by source paper "funding information in web of science an updated overview"):
  with members:
    - "solving multiple criteria r d project selection problems with a data driven evidential reasoning rule"
    - "missing author address information in web of science an explorative study"
    - "funding acknowledgment analysis queries and caveats"
    - "the sum of it all revealing collaboration patterns by combining authorship and acknowledgements"
    - "the journal coverage of web of science and scopus a comparative analysis"

Connected through hyperedge HE-3 (induced by source paper "making visible the invisible through the analysis of acknowledgements in the humanities"):
  with members:
    - "the sum of it all revealing collaboration patterns by combining authorship and acknowledgements"

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)