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 "china may need to support more small teams in scientific research"):
  Abstract: Modern science is dominated by scientific productions from teams. Large teams have demonstrated a clear advantage over small teams in applying for research funding, performing complicated research tasks and producing research works with high impact. Recent research, however, shows that both large and small teams have their own merits. Small teams tend to expand the frontier of knowledge by creating disruptive research outcomes, while large teams are more apt to work in the established field and develop existing problems. Given different roles of big and small teams in research, the extent to which a country's scientific work is carried out by big/small teams is of great importance. Here, we analyze over 26 million papers from Web of Science published from 2000 to 2017. We find that China's research output is more dominated by big teams than the rest of the world. It is indeed a global trend that more papers are done by big teams. However, the drop in small team output is much steeper in China. More importantly, as research teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries. Papers by big teams tend to receive more citations, but this alone is insufficient to explain the dominance of big teams in China, because the citation boost is roughly the same in every country. However, using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which urges a need to balance small and big teams.

Includes members:
  Member "practical method to reclassify web of science articles into unique subject categories and broad disciplines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "comparing the impact of subfields in scientific journals")
  Member "the preeminence of ethnic diversity in scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural embeddings of scholarly periodicals reveal complex disciplinary organizations")
  Member "collaboration diversity and scientific impact":
    also connected through other hyperedges:
      (none)
  Member "funding acknowledgment analysis queries and caveats":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "funding information in web of science an updated overview")
  Member "the relationship among research productivity research collaboration and their determinants":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "authorship analysis of specialized vs diversified research output")
  Member "characterization description and considerations for the use of funding acknowledgement data in web of science":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "funding information in web of science an updated overview")
  Member "understanding the group dynamics and success of teams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "massive multi agent data driven simulations of the github ecosystem")
  Member "field normalized citation impact indicators and the choice of an appropriate counting method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "democracy globalization and science")

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)