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: "a hierarchical recurrent neural network for symbolic melody generation"
  Abstract: In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty for designing a good model. In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time. Specifically, the three subnetworks generate bar profiles, beat profiles and notes in turn, and the output of the high-level subnetworks are fed into the low-level subnetworks, serving as guidance for generating the finer time-scale melody components in low-level subnetworks. Two human behavior experiments demonstrate the advantage of this structure over the single-layer LSTM which attempts to learn all hidden structures in melodies. Compared with the state-of-the-art models MidiNet and MusicVAE, the hierarchical recurrent neural network produces better melodies evaluated by humans.

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"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"

Connected through hyperedge HE-2 (induced by source paper "conditional lstm gan for melody generation from lyrics"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "neural melody composition from lyrics"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "algorithmic songwriting with alysia"
    - "conditional generative adversarial nets"
    - "ai methods in algorithmic composition a comprehensive survey"

Connected through hyperedge HE-3 (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 unit selection methodology for music generation using deep neural networks"
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-4 (induced by source paper "melody generation using an interactive evolutionary algorithm"):
  with members:
    - "modeling musical context with word2vec"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

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)