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: "measuring the evolution of contemporary western popular music"
  Abstract: Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. Here we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness in contemporary western popular music. Many of these patterns and metrics have been consistently stable for a period of more than fifty years. However, we prove important changes or trends related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. This suggests that our perception of the new would be rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

Connected through hyperedge HE-1 (induced by source paper "quantifying novelty and influence and the patterns of paradigm shifts"):
  with members:
    - "character aware neural language models"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "evolution of the informational complexity of contemporary western music"):
  with members:
    - "identifying cover songs using information theoretic measures of similarity"

Connected through hyperedge HE-3 (induced by source paper "music popularity metrics characteristics and audio based prediction"):
  with members:
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "predicting popularity of online videos using support vector regression"
    - "unravelling the impact of temporal and geographical locality in content caching systems"

Connected through hyperedge HE-4 (induced by source paper "labelled network subgraphs reveal stylistic subtleties in written texts"):
  with members:
    - "on the modeling of musical solos as complex networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "revisiting the problem of audio based hit song prediction using convolutional neural networks"):
  with members:
    - "automatic tagging using deep convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "a theoretical analysis of ndcg type ranking measures"

Connected through hyperedge HE-6 (induced by source paper "labelled network motifs reveal stylistic subtleties in written texts"):
  with members:
    - "on the modeling of musical solos as complex networks"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"

Connected through hyperedge HE-7 (induced by source paper "rank diversity of languages generic behavior in computational linguistics"):
  with members:
    - "complexity measurement of natural and artificial languages"

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)