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: "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.

Connected through hyperedge HE-1 (induced by source paper "learning to traverse latent spaces for musical score inpainting"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "this time with feeling learning expressive musical performance"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "the machine learning algorithm as creative musical tool"
    - "a predictive model for music based on learned interval representations"
    - "music transformer"
    - "audio to score alignment using transposition invariant features"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-2 (induced by source paper "polyphonic music composition with lstm neural networks and reinforcement learning"):
  with members:
    - "composing music with grammar argumented neural networks and note level encoding"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"
    - "adam a method for stochastic optimization"
    - "ai methods in algorithmic composition a comprehensive survey"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-3 (induced by source paper "conditional lstm gan for melody generation from lyrics"):
  with members:
    - "neural melody composition from lyrics"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "a hierarchical recurrent neural network for symbolic melody generation"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "algorithmic songwriting with alysia"
    - "conditional generative adversarial nets"
    - "ai methods in algorithmic composition a comprehensive survey"

Connected through hyperedge HE-4 (induced by source paper "smart edition of midi files"):
  with members:
    - "deep learning techniques for music generation a survey"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-5 (induced by source paper "handwriting styles benchmarks and evaluation metrics"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "tristounet triplet loss for speaker turn embedding"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "deep learning techniques for music generation a survey"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "conditioning deep generative raw audio models for structured automatic music"
    - "deepj style specific music generation"
    - "music style transfer issues a position paper"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"
    - "music transformer"

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)