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: "the challenge of realistic music generation modelling raw audio at scale"
  Abstract: Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds.

Connected through hyperedge HE-1 (induced by source paper "hierarchical autoregressive image models with auxiliary decoders"):
  with members:
    - "preventing posterior collapse with delta vaes"
    - "deep contextualized word representations"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "waveflow a compact flow based model for raw audio"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "high fidelity speech synthesis with adversarial networks"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"
    - "waveglow a flow based generative network for speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "stcn stochastic temporal convolutional networks"):
  with members:
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "fast decoding in sequence models using discrete latent variables"
    - "deepwriting making digital ink editable via deep generative modeling"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "filtering variational objectives"
    - "learning hierarchical features from generative models"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "generating long sequences with sparse transformers"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "character level language modeling with deeper self attention"
    - "music transformer"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-5 (induced by source paper "generating diverse high fidelity images with vq vae 2"):
  with members:
    - "classification accuracy score for conditional generative models"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "attention is all you need"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "seq u net a one dimensional causal u net for efficient sequence modelling"):
  with members:
    - "generating long sequences with sparse transformers"
    - "efficient neural audio synthesis"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-7 (induced by source paper "capacity allocation analysis of neural networks a tool for principled architecture design"):
  with members:
    - "capacity allocation through neural network layers"
    - "deepbase deep inspection of neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "efficient neural audio synthesis"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-8 (induced by source paper "learning neurosymbolic generative models via program synthesis"):
  with members:
    - "houdini lifelong learning as program synthesis"
    - "programmatically interpretable reinforcement learning"
    - "verifiable reinforcement learning via policy extraction"
    - "selecting representative examples for program synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "texturegan controlling deep image synthesis with texture patches"
    - "towards deeper understanding of variational autoencoding models"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)