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: "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.

Connected through hyperedge HE-1 (induced by source paper "summarization of films and documentaries based on subtitles and scripts"):
  with members:
    - "keyphrase cloud generation of broadcast news"
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"

Connected through hyperedge HE-2 (induced by source paper "an information theoretic approach to machine oriented music summarization"):
  with members:
    - "summarization of films and documentaries based on subtitles and scripts"
    - "using generic summarization to improve music information retrieval tasks"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-3 (induced by source paper "using generic summarization to improve music information retrieval tasks"):
  with members:
    - "codebook based audio feature representation for music information retrieval"
    - "mlpack a scalable c machine learning library"
    - "lexrank graph based lexical centrality as salience in text summarization"

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.IR(Information Retrieval)