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 "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.

Includes members:
  Member "on the calculation of percentile based bibliometric indicators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "a new methodology for constructing a publication level classification system of science":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practical method to reclassify web of science articles into unique subject categories and broad disciplines")
  Member "towards an explanatory and computational theory of scientific discovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "early detection of research trends")

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)