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 "learning to traverse latent spaces for musical score inpainting"):
  Abstract: Music Inpainting is the task of filling in missing or lost information in a piece of music. We investigate this task from an interactive music creation perspective. To this end, a novel deep learning-based approach for musical score inpainting is proposed. The designed model takes both past and future musical context into account and is capable of suggesting ways to connect them in a musically meaningful manner. To achieve this, we leverage the representational power of the latent space of a Variational Auto-Encoder and train a Recurrent Neural Network which learns to traverse this latent space conditioned on the past and future musical contexts. Consequently, the designed model is capable of generating several measures of music to connect two musical excerpts. The capabilities and performance of the model are showcased by comparison with competitive baselines using several objective and subjective evaluation methods. The results show that the model generates meaningful inpaintings and can be used in interactive music creation applications. Overall, the method demonstrates the merit of learning complex trajectories in the latent spaces of deep generative models.

Includes members:
  Member "music generation by deep learning challenges and directions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polyphonic music composition with lstm neural networks and reinforcement learning")
  Member "midi vae modeling dynamics and instrumentation of music with applications to style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning style aware symbolic music representations by adversarial autoencoders")
  Member "this time with feeling learning expressive musical performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "music generation by deep learning challenges and directions")
  Member "the machine learning algorithm as creative musical tool":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "music generation by deep learning challenges and directions")
  Member "a predictive model for music based on learned interval representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling musical structure with artificial neural networks")
  Member "music transformer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stand alone self attention in vision models")
  Member "audio to score alignment using transposition invariant features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning complex basis functions for invariant representations of audio")

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.LG(Machine Learning)