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 modeling of musical solos as complex networks"
  Abstract: A melodic track can be represented as a network; this allows calculating general measures characterizing it.We show that networks associated to different guitar solos have different topologies, and thus, different characteristics.We show how complex network theory can be profitably employed in a novel application scenario.We calculate different measures on the considered networks, and present aggregate results characterizing related performers.Outcomes of this study can have an impact on music classification, didactics, automatic music generation and entertainment. Notes in a musical piece are building blocks employed in non-random ways to create melodies. It is the "interaction" among a limited amount of notes that allows constructing the variety of musical compositions that have been written in centuries and within different cultures. Networks are a modeling tool that is commonly employed to represent a set of entities interacting in some way. Thus, notes composing a melody can be seen as nodes of a network that are connected whenever these are played in sequence. The outcome of such a process results in a directed graph. By using complex network theory, some main metrics of musical graphs can be measured, which characterize the related musical pieces. In this paper, we define a framework to represent melodies as networks. Then, we provide an analysis on a set of guitar solos performed by main musicians. Results of this study indicate that the presented model can have an impact on audio and multimedia applications such as music classification, identification, e-learning, automatic music generation, multimedia entertainment.

Connected through hyperedge HE-1 (induced by source paper "on the complex network structure of musical pieces analysis of some use cases from different music genres"):
  with members:
    - "guitar solos as networks"

Connected through hyperedge HE-2 (induced by source paper "labelled network subgraphs reveal stylistic subtleties in written texts"):
  with members:
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "authorship attribution via network motifs identification"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "authorship attribution using word network features"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"
    - "measuring the evolution of contemporary western popular music"

Connected through hyperedge HE-3 (induced by source paper "labelled network motifs reveal stylistic subtleties in written texts"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "measuring the evolution of contemporary western popular music"

Connected through hyperedge HE-4 (induced by source paper "clustering of musical pieces through complex networks an assessment over guitar solos"):
  with members:
    - "guitar solos as networks"

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.SD(Sound)