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 "investigation of partition cells as a structural basis suitable for assessments of individual scientists"):
  Abstract: Individual, excellent scientists have become increasingly important in the research funding landscape. Accurate bibliometric measures of an individual's performance could help identify excellent scientists, but still present a challenge. One crucial aspect in this respect is an adequate delineation of the sets of publications that determine the reference values to which a scientist's publication record and its citation impact should be compared. The structure of partition cells formed by intersecting fixed subject categories in a database has been proposed to approximate a scientist's specialty more closely than can be done with the broader subject categories. This paper investigates this cell structure's suitability as an underlying basis for methodologies to assess individual scientists, from two perspectives: (1) Proximity to the actual structure of publication records of individual scientists: The distribution and concentration of publications over the highly fragmented structure of partition cells are examined for a sample of ERC grantees; (2) Proximity to customary levels of accuracy: Differences in commonly used reference values (mean expected number of citations per publication, and threshold number of citations for highly cited publications) between adjacent partition cells are compared to differences in two other dimensions: successive publication years and successive citation window lengths. Findings from both perspectives are in support of partition cells rather than the larger subject categories as a journal based structure on which to construct and apply methodologies for the assessment of highly specialized publication records such as those of individual scientists.

Includes members:
  Member "groups of highly cited publications stability in content with citation window length":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "research assessment by percentile based double rank analysis")
  Member "partition based field normalization an approach to highly specialized publication records":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do altmetrics assess societal impact in a comparable way to case studies an empirical test of the convergent validity of altmetrics based on data from the uk research excellence framework ref")

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)