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: "ai methods in algorithmic composition a comprehensive survey"
  Abstract: Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

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 "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"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-3 (induced by source paper "counterpoint by convolution"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "neural autoregressive distribution estimation"
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales generation"
    - "pixel recurrent neural networks"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "the bach doodle approachable music composition with machine learning at scale"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "counterpoint by convolution"
    - "a functional taxonomy of music generation systems"
    - "deep learning techniques for music generation a survey"
    - "neural autoregressive distribution estimation"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-5 (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"
    - "song from pi a musically plausible network for pop music generation"
    - "music transcription modelling and composition using deep learning"

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"
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"
    - "adam a method for stochastic optimization"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-7 (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"
    - "a hierarchical recurrent neural network for symbolic melody generation"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "algorithmic songwriting with alysia"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "computer assisted composition in continuous time"):
  with members:
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "symbolic music data version 1 0"
    - "computer assisted composition with recurrent 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.AI(Artificial Intelligence)