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: "deep learning for music"
  Abstract: Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNN-RBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone.

Connected through hyperedge HE-1 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "neuralogram a deep neural network based representation for audio signals"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "conditional end to end audio transforms"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-3 (induced by source paper "learning to model aspects of hearing perception using neural loss functions"):
  with members:
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "conditional end to end audio transforms"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "audio adversarial examples targeted attacks on speech to text"
    - "learned perceptual image enhancement"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-4 (induced by source paper "friend collaborator student manager how design of an ai driven game level editor affects creators"):
  with members:
    - "co creative level design via machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "teaching meaningful explanations"
    - "rationalization a neural machine translation approach to generating natural language explanations"
    - "procedural content generation via machine learning pcgml"
    - "a neural representation of sketch drawings"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "text based lstm networks for automatic music composition"

Connected through hyperedge HE-5 (induced by source paper "conditioning deep generative raw audio models for structured automatic music"):
  with members:
    - "a neural parametric singing synthesizer"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "youtube 8m a large scale video classification benchmark"
    - "deepbach a steerable model for bach chorales generation"
    - "pixel recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "polyphonic music generation by modeling temporal dependencies using a rnn dbn"

Connected through hyperedge HE-6 (induced by source paper "deep learning techniques for music generation a survey"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "predicting process behaviour using deep learning"):
  with members:
    - "lstm a search space odyssey"
    - "comparative study of deep learning software frameworks"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "text based lstm networks for automatic music composition"
    - "wavenet a generative model for raw audio"
    - "visualizing and understanding neural models in nlp"
    - "understanding neural networks through deep visualization"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "jambot music theory aware chord based generation of polyphonic music with lstms"):
  with members:
    - "samplernn an unconditional end to end neural audio generation model"
    - "text based lstm networks for automatic music composition"
    - "song from pi a musically plausible network for pop music generation"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales generation"
    - "a neural algorithm of artistic style"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)