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: "song from pi a musically plausible network for pop music generation"
  Abstract: We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and chords. We conduct several human studies that show strong preference of our generated music over that produced by the recent method by Google. We additionally show two applications of our framework: neural dancing and karaoke, as well as neural story singing.

Connected through hyperedge HE-1 (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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation"):
  with members:
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "generative cooperative net for image generation and data augmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "improved training of wasserstein gans"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"

Connected through hyperedge HE-3 (induced by source paper "classical music generation in distinct dastgahs with alimnet acgan"):
  with members:
    - "instrument independent dastgah recognition of iranian classical music using azarnet"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "svsgan singing voice separation via generative adversarial network"
    - "tac gan text conditioned auxiliary classifier generative adversarial network"
    - "neural discrete representation learning"

Connected through hyperedge HE-4 (induced by source paper "improving automatic jazz melody generation by transfer learning techniques"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "transfer learning for music classification and regression tasks"
    - "representation learning of music using artist labels"
    - "what makes imagenet good for transfer learning"
    - "music transcription modelling and composition using deep learning"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-5 (induced by source paper "explicitly conditioned melody generation a case study with interdependent rnns"):
  with members:
    - "on the convergence of adam and beyond"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "deepbach a steerable model for bach chorales generation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "polyphonic music composition with lstm neural networks and reinforcement learning"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "composing music with grammar argumented neural networks and note level encoding"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "deepbach a steerable model for bach chorales generation"
    - "adam a method for stochastic optimization"
    - "ai methods in algorithmic composition a comprehensive survey"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-7 (induced by source paper "progressive generative adversarial binary networks for music generation"):
  with members:
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "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"
    - "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-8 (induced by source paper "gankyoku a generative adversarial network for shakuhachi music"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "improved training of wasserstein gans"
    - "a hierarchical recurrent neural network for symbolic melody generation"
    - "a unit selection methodology for music generation using deep neural networks"
    - "deepbach a steerable model for bach chorales generation"

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)