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 "contextualization of topics browsing through the universe of bibliographic information"):
  Abstract: This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records (Koopman et al. in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. doi:10.1145/2702613.2732781, 2015b). In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue "Same data, different results"--the better understanding of topic (re-)construction by different bibliometric approaches. For the case of the Astro dataset of 111,616 articles in astronomy and astrophysics, a new instantiation of the interactive exploring tool, LittleAriadne, has been created. This paper contributes to the overall challenge to delineate and define topics in two different ways. First, we produce two clustering solutions based on vector representations of articles in a lexical space. These vectors are built on semantic indexing of entities associated with those articles. Second, we discuss how LittleAriadne can be used to browse through the network of topical terms, authors, journals, citations and various cluster solutions of the Astro dataset. More specifically, we treat the assignment of an article to the different clustering solutions as an additional element of its bibliographic record. Keeping the principle of semantic indexing on the level of such an extended list of entities of the bibliographic record, LittleAriadne in turn provides a visualization of the context of a specific clustering solution. It also conveys the similarity of article clusters produced by different algorithms, hence representing a complementary approach to other possible means of comparison.

Includes members:
  Member "citation based clustering of publications using citnetexplorer and vosviewer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visualizing the context of citations referencing papers published by eugene garfield a new type of keyword co occurrence analysis")
  Member "mutual information based labelling and comparing clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how to interpret algorithmically constructed topical structures of research specialties a case study comparing an internal and an external mapping of the topical structure of invasion biology")
  Member "clustering articles based on semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edge computing for user centric secure search on cloud based encrypted big data")
  Member "ariadne s thread interactive navigation in a world of networked information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "clustering articles based on semantic similarity")
  Member "contextualization of topics browsing through terms authors journals and cluster allocations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clustering articles based on semantic similarity")
  Member "scientometrics and information retrieval weak links revitalized":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "report on the 8th international workshop on bibliometric enhanced information retrieval bir 2019")
  Member "the semantic mapping of words and co words in contexts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks")

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)