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 "smart edition of midi files"):
  Abstract: We address the issue of editing musical performance data, in particular MIDI files representing human musical performances. Editing such sequences raises specific issues due to the ambiguous nature of musical objects. The first source of ambiguity is that musicians naturally produce many deviations from the metrical frame. These deviations may be intentional or subconscious, but they play an important role in conveying the groove or feeling of a performance. Relations between musical elements are also usually implicit, creating even more ambiguity. A note is in relation with the surrounding notes in many possible ways: it can be part of a melodic pattern, it can also play a harmonic role with the simultaneous notes, or be a pedal-tone. All these aspects play an essential role that should be preserved, as much as possible, when editing musical sequences. #R##N#In this paper, we contribute specifically to the problem of editing non-quantized, metrical musical sequences represented as MIDI files. We first list of number of problems caused by the use of naive edition operations applied to performance data, using a motivating example. We then introduce a model, called Dancing MIDI, based on 1) two desirable, well-defined properties for edit operations and 2) two well-defined operations, Split and Concat, with an implementation. We show that our model formally satisfies the two properties, and that it prevents most of the problems that occur with naive edit operations on our motivating example, as well as on a real-world example using an automatic harmonizer.

Includes members:
  Member "music generation by deep learning challenges and directions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to traverse latent spaces for musical score inpainting")
  Member "deep learning techniques for music generation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music generation by deep learning challenges and directions")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "deepbach a steerable model for bach chorales generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)