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 "automatic melody harmonization with triad chords a comparative study"):
  Abstract: Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating and comparing the performance of a set of canonical approaches to this task, including a template matching based model, a hidden Markov based model, a genetic algorithm based model, and two deep learning based models. The evaluation is conducted on a dataset of 9,226 melody/chord pairs we newly collect for this study, considering up to 48 triad chords, using a standardized training/test split. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants.

Includes members:
  Member "lead sheet generation and arrangement by conditional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rhythm chord and melody generation for lead sheets using recurrent neural networks")
  Member "generative statistical models with self emergent grammar of chord sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi step chord sequence prediction based on aggregated multi scale encoder decoder network")
  Member "chord generation from symbolic melody using blstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rhythm chord and melody generation for lead sheets using recurrent neural networks")
  Member "deep learning techniques for music generation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "music generation by deep learning challenges and directions")
  Member "ai methods in algorithmic composition a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "music generation by deep learning challenges and directions")

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.SD(Sound)