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 "summarization of films and documentaries based on subtitles and scripts"):
  Abstract: We study the behavior of automatic summarization for films and documentaries.Well-known extractive summarization algorithms are ranked for this task.Assessment of strategies for effective extractive summarization in these domains.Quantitative results are presented for relevant experiments.Qualitative assessment is also provided (concerning the best approaches). We assess the performance of generic text summarization algorithms applied to films and documentaries, using extracts from news articles produced by reference models of extractive summarization. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries against news abstracts, plot summaries, and synopses. We show that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films. Despite the different nature of films and documentaries, their relative behavior is in accordance with that obtained for news articles.

Includes members:
  Member "on the application of generic summarization algorithms to music":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an information theoretic approach to machine oriented music summarization")
  Member "keyphrase cloud generation of broadcast news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a review of keyphrase extraction")
  Member "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "lexrank graph based lexical centrality as salience in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating representative headlines for news stories")
  Member "inferring strategies for sentence ordering in multidocument news summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural 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.CL(Computation and Language)