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: "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
  Abstract: It has been shown recently that deep convolutional generative adversarial networks (GANs) can learn to generate music in the form of piano-rolls, which represent music by binary-valued time-pitch matrices. However, existing models can only generate real-valued piano-rolls and require further post-processing, such as hard thresholding (HT) or Bernoulli sampling (BS), to obtain the final binary-valued results. In this paper, we study whether we can have a convolutional GAN model that directly creates binary-valued piano-rolls by using binary neurons. Specifically, we propose to append to the generator an additional refiner network, which uses binary neurons at the output layer. The whole network is trained in two stages. Firstly, the generator and the discriminator are pretrained. Then, the refiner network is trained along with the discriminator to learn to binarize the real-valued piano-rolls the pretrained generator creates. Experimental results show that using binary neurons instead of HT or BS indeed leads to better results in a number of objective measures. Moreover, deterministic binary neurons perform better than stochastic ones in both objective measures and a subjective test. The source code, training data and audio examples of the generated results can be found at this https URL .

Connected through hyperedge HE-1 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"
    - "online learning with gated linear networks"

Connected through hyperedge HE-2 (induced by source paper "lakhnes improving multi instrumental music generation with cross domain pre training"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "counterpoint by convolution"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "the nes music database a multi instrumental dataset with expressive performance attributes"
    - "deepj style specific music generation"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "biosignal generation and latent variable analysis with recurrent generative adversarial networks"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "unrolled generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "adam a method for stochastic optimization"
    - "advances in optimizing recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "classical music prediction and composition by means of variational autoencoders"):
  with members:
    - "non monotonic sequential text generation"
    - "generative timbre spaces regularizing variational auto encoders with perceptual metrics"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "learning latent representations for speech generation and transformation"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-5 (induced by source paper "progressive generative adversarial binary networks for music generation"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "song from pi a musically plausible network for pop music generation"
    - "music transcription modelling and composition using deep learning"
    - "hierarchical multiscale recurrent neural networks"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-6 (induced by source paper "midi sandwich2 rnn based hierarchical multi modal fusion generation vae networks for multi track symbolic music generation"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "large scale gan training for high fidelity natural image synthesis"
    - "symbolic music genre transfer with cyclegan"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-7 (induced by source paper "modeling melodic feature dependency with modularized variational auto encoder"):
  with members:
    - "bachprop learning to compose music in multiple styles"
    - "deepj style specific music generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "jambot music theory aware chord based generation of polyphonic music with lstms"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "samplernn an unconditional end to end neural audio generation model"

Connected through hyperedge HE-8 (induced by source paper "learning disentangled representations for timber and pitch in music audio"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "exploring disentangled feature representation beyond face identification"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "learning to decompose and disentangle representations for video prediction"
    - "performancenet score to audio music generation with multi band convolutional residual network"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

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)