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 techniques for music generation a survey"
  Abstract: This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: #R##N#Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). #R##N#Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. #R##N#Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. #R##N#Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. #R##N#Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. #R##N#For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.

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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "a functional taxonomy of music generation systems"

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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "style transfer from non parallel text by cross alignment"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"

Connected through hyperedge HE-5 (induced by source paper "continuous melody generation via disentangled short term representations and structural conditions"):
  with members:
    - "inspecting and interacting with meaningful music representations using vae"
    - "deep music analogy via latent representation disentanglement"
    - "learning to traverse latent spaces for musical score inpainting"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "the effect of explicit structure encoding of deep neural networks for symbolic music generation"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales generation"

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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "chord generation from symbolic melody using blstm networks"
    - "sampling variations of lead sheets"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "automatic melody harmonization with triad chords a comparative study"):
  with members:
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "generative statistical models with self emergent grammar of chord sequences"
    - "chord generation from symbolic melody using blstm networks"
    - "ai methods in algorithmic composition a comprehensive survey"

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)