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: "midinet a convolutional generative adversarial network for symbolic domain music generation"
  Abstract: Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural networks (CNNs) can also generate realistic musical waveforms in the audio domain. Following this light, we investigate using CNNs for generating melody (a series of MIDI notes) one bar after another in the symbolic domain. In addition to the generator, we use a discriminator to learn the distributions of melodies, making it a generative adversarial network (GAN). Moreover, we propose a novel conditional mechanism to exploit available prior knowledge, so that the model can generate melodies either from scratch, by following a chord sequence, or by conditioning on the melody of previous bars (e.g. a priming melody), among other possibilities. The resulting model, named MidiNet, can be expanded to generate music with multiple MIDI channels (i.e. tracks). We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google's MelodyRNN models, each time using the same priming melody. Result shows that MidiNet performs comparably with MelodyRNN models in being realistic and pleasant to listen to, yet MidiNet's melodies are reported to be much more interesting.

Connected through hyperedge HE-1 (induced by source paper "music generation by deep learning challenges and directions"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "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"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "a functional taxonomy of music generation systems"
    - "interactive music generation with positional constraints using anticipation rnns"

Connected through hyperedge HE-2 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning"):
  with members:
    - "the bach doodle approachable music composition with machine learning at scale"
    - "counterpoint by convolution"
    - "gansynth adversarial neural audio synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "an actor critic algorithm for sequence prediction"
    - "c rnn gan continuous recurrent neural networks with adversarial training"

Connected through hyperedge HE-3 (induced by source paper "mg vae deep chinese folk songs generation with specific regional styles"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "this time with feeling learning expressive musical performance"
    - "neural melody composition from lyrics"
    - "deepj style specific music generation"
    - "music style transfer issues a position paper"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "learning disentangled representations for timber and pitch in music audio"
    - "a functional taxonomy of music generation systems"

Connected through hyperedge HE-4 (induced by source paper "learning style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-5 (induced by source paper "pop music transformer generating music with rhythm and harmony"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "encoding musical style with transformer autoencoders"
    - "lakhnes improving multi instrumental music generation with cross domain pre training"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "this time with feeling learning expressive musical performance"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-6 (induced by source paper "learning a latent space of style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-7 (induced by source paper "rhythm chord and melody generation for lead sheets using recurrent neural networks"):
  with members:
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "chord generation from symbolic melody using blstm networks"
    - "sampling variations of lead sheets"
    - "deep learning techniques for music generation a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "application of deep learning in generating desired design options experiments using synthetic training dataset"):
  with members:
    - "conditional generative adversarial nets"

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)