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 "music generation by deep learning challenges and directions"):
  Abstract: In addition to traditional tasks such as prediction, classification and translation, deep learning is receiving growing attention as an approach for music generation, as witnessed by recent research groups such as Magenta at Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is in using the capacity of deep learning architectures and training techniques to automatically learn musical styles from arbitrary musical corpora and then to generate samples from the estimated distribution. However, a direct application of deep learning to generate content rapidly reaches limits as the generated content tends to mimic the training set without exhibiting true creativity. Moreover, deep learning architectures do not offer direct ways for controlling generation (e.g., imposing some tonality or other arbitrary constraints). Furthermore, deep learning architectures alone are autistic automata which generate music autonomously without human user interaction, far from the objective of interactively assisting musicians to compose and refine music. Issues such as control, structure, creativity and interactivity are the focus of our analysis. In this paper, we select some limitations of a direct application of deep learning to music generation and analyze why the issues are not fulfilled and how to address them by possible approaches. Various examples of recent systems are cited as examples of promising directions.

Includes members:
  Member "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling musical structure with artificial neural networks")
  Member "music style transfer issues a position paper":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mg vae deep chinese folk songs generation with specific regional styles")
  Member "a hierarchical latent vector model for learning long term structure in music":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep generative model for graph layout")
  Member "the machine learning algorithm as creative musical tool":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to traverse latent spaces for musical score inpainting")
  Member "music style transfer a position paper":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supervised symbolic music style translation using synthetic data")
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning")
  Member "can creative adversarial networks generating art by learning about styles and deviating from style norms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "machine learning based co creative design framework")
  Member "a functional taxonomy of music generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mg vae deep chinese folk songs generation with specific regional styles")

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)