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: "heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation"
  Abstract: While bibliometrics are widely used for research evaluation purposes, a common theoretical framework for conceptually understanding, empirically studying, and effectively teaching its usage is lacking. In this paper, we outline such a framework: the fast-and-frugal heuristics research program, proposed originally in the context of the cognitive and decision sciences, lends itself particularly well for understanding and investigating the usage of bibliometrics in research evaluations. Such evaluations represent judgments under uncertainty in which typically not all possible options, their consequences, and those consequences’ probabilities of occurring may be known. In these situations of incomplete information, candidate descriptive and prescriptive models of human behavior are heuristics. Heuristics are simple strategies that, by exploiting the structure of environments, can aid people to make smart decisions. Relying on heuristics does not mean trading off accuracy against effort: while reducing complexity, heuristics can yield better decisions than more information-greedy procedures in many decision environments. The prescriptive power of heuristics is documented in a cross-disciplinary literature, cutting across medicine, crime, business, sports, and other domains. We outline the fast-and-frugal heuristics research program, provide examples of past empirical work on heuristics outside the field of bibliometrics, explain why heuristics may be especially suitable for studying the usage of bibliometrics, and propose a corresponding conceptual framework.

Connected through hyperedge HE-1 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better"):
  with members:
    - "creativity in science and the link to cited references is the creative potential of papers reflected in their cited references"
    - "plots for visualizing paper impact and journal impact of single researchers in a single graph"
    - "how are excellent highly cited papers defined in bibliometrics a quantitative analysis of the literature"
    - "the substantive and practical significance of citation impact differences between institutions guidelines for the analysis of percentiles using effect sizes and confidence intervals"
    - "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"
    - "how to evaluate individual researchers working in the natural and life sciences meaningfully a proposal of methods based on percentiles of citations"
    - "which percentile based approach should be preferred for calculating normalized citation impact values an empirical comparison of five approaches including a newly developed citation rank approach p100"
    - "on the calculation of percentile based bibliometric indicators"

Connected through hyperedge HE-2 (induced by source paper "do we measure novelty when we analyze unusual combinations of cited references a validation study of bibliometric novelty indicators based on f1000prime data"):
  with members:
    - "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators"
    - "creativity in science and the link to cited references is the creative potential of papers reflected in their cited references"
    - "identifying potential breakthrough publications using refined citation analyses three related explorative approaches"
    - "coauthorship networks a directed network approach considering the order and number of coauthors"
    - "the effects of research level and article type on the differences between citation metrics and f1000 recommendations"
    - "the validation of advanced bibliometric indicators through peer assessments a comparative study using data from incites and f1000"

Connected through hyperedge HE-3 (induced by source paper "h alpha the scientist as chimpanzee or bonobo"):
  with members:
    - "h α an index to quantify an individual s scientific leadership"
    - "the inconsistency of the h index"

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)