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 "on the application of generic summarization algorithms to music"):
  Abstract: AbstractSeveral generic summarization algorithms were developed in the past and successfully applied inﬁelds such as text and speech summarization. In this paper, w e review and apply these algorithms tomusic. To evaluate this summarization’s performance, we adopt an extrinsic approach: we compare a FadoGenre Classiﬁer’s performance using truncated contiguous clips against the summaries extracted withthose algorithms on 2 different datasets. We show that Maximal Marginal Relevance (MMR), LexRankand Latent Semantic Analysis (LSA) all improve classiﬁcati on performance in both datasets used fortesting. I. I NTRODUCTION Several algorithms to summarize music have been published [1]–[8], mainly for popular music songswhose structure is repetitive enough. However, those algorithms were devised with the goal of producinga thumbnail of a song as its summary, the same way an image’s thumbnail is that image’s summary.Therefore, the goal is to output a shorter version of the original song so that people can quickly getthe gist of the whole piece without listening to all of it. These algorithms usually extract continuoussegments because of their human consumption-oriented purpose.

Includes members:
  Member "automatic fado music classification":
    also connected through other hyperedges:
      (none)
  Member "mlpack a scalable c machine learning library":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on explaining machine learning models by evolving crucial and compact features")
  Member "lexrank graph based lexical centrality as salience in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating representative headlines for news stories")

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.IR(Information Retrieval)