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: "identifying potential breakthrough publications using refined citation analyses three related explorative approaches"
  Abstract: The article presents three advanced citation-based methods used to detect potential breakthrough papers among very highly cited papers. We approach the detection of such papers from three different perspectives in order to provide different typologies of breakthrough papers. In all three cases we use the classification of scientific publications developed at CWTS based on direct citation relationships. This classification establishes clusters of papers at three levels of aggregation. Papers are clustered based on their similar citation orientations and it is assumed that they are focused on similar research interests. We use the clustering as the context for detecting potential breakthrough papers. We utilize the Characteristics Scores and Scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited followers, papers not considered breakthroughs in themselves. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited paper in a micro-cluster is considered as a potential breakthrough paper (M1); as well as two conceptually different methods, one that detects potential breakthrough papers among the two percent highest cited papers according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS two percent filter, knowledge diffusion is also taken in as an extra parameter (M2b). The advance citation-based methods are explored and evaluated using specifically validated publication sets linked to different Danish funding instruments including centres of excellence.

Connected through hyperedge HE-1 (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"
    - "heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation"
    - "creativity in science and the link to cited references is the creative potential of papers reflected in their cited references"
    - "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-2 (induced by source paper "research assessment by percentile based double rank analysis"):
  with members:
    - "citation score normalized by cited references csncr the introduction of a new citation impact indicator"
    - "efficiency of research performance and the glass researcher"
    - "a review of the literature on citation impact indicators"
    - "regression for citation data an evaluation of different methods"
    - "how are excellent highly cited papers defined in bibliometrics a quantitative analysis of the literature"
    - "distributions for cited articles from individual subjects and years"
    - "brics countries and scientific excellence a bibliometric analysis of most frequently cited papers"
    - "how do you define and measure research productivity"

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)