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: "text based lstm networks for automatic music composition"
  Abstract: In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. The proposed system can be used for fully automatic composition or as semi-automatic systems that help humans to compose music by controlling a diversity parameter of the model.

Connected through hyperedge HE-1 (induced by source paper "modeling musical structure with artificial neural networks"):
  with members:
    - "high level control of drum track generation using learned patterns of rhythmic interaction"
    - "learning complex basis functions for invariant representations of audio"
    - "counterpoint by convolution"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a predictive model for music based on learned interval representations"
    - "improving content invariance in gated autoencoders for 2d and 3d object rotation"
    - "deepbach a steerable model for bach chorales generation"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier"):
  with members:
    - "deep learning for time series classification a review"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "deepid3 face recognition with very deep neural networks"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "friend collaborator student manager how design of an ai driven game level editor affects creators"):
  with members:
    - "co creative level design via machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "teaching meaningful explanations"
    - "rationalization a neural machine translation approach to generating natural language explanations"
    - "procedural content generation via machine learning pcgml"
    - "a neural representation of sketch drawings"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "deep learning for music"

Connected through hyperedge HE-4 (induced by source paper "feature engineering and forecasting via derivative free optimization and ensemble of sequence to sequence networks with applications in renewable energy"):
  with members:
    - "multivariate multistep forecasting reconstruction and feature selection of ocean waves via recurrent and sequence to sequence networks"
    - "lstm a search space odyssey"
    - "the microsoft 2017 conversational speech recognition system"
    - "black box optimization in machine learning with trust region based derivative free algorithm"
    - "lung cancer screening using adaptive memory augmented recurrent networks"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "learning to diagnose with lstm recurrent neural networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi step chord sequence prediction based on aggregated multi scale encoder decoder network"):
  with members:
    - "using musical relationships between chord labels in automatic chord extraction tasks"
    - "improved chord recognition by combining duration and harmonic language models"
    - "generative statistical models with self emergent grammar of chord sequences"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "live orchestral piano a system for real time orchestral music generation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep speare a joint neural model of poetic language meter and rhyme"):
  with members:
    - "selective encoding for abstractive sentence summarization"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "chinese song iambics generation with neural attention based model"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "music transcription modelling and composition using deep learning"
    - "generative choreography using deep learning"

Connected through hyperedge HE-7 (induced by source paper "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"):
  with members:
    - "music transcription modelling and composition using deep learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "advances in optimizing recurrent networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-8 (induced by source paper "deepdrum an adaptive conditional neural network"):
  with members:
    - "talking drums generating drum grooves with neural networks"
    - "deep learning techniques for music generation a survey"
    - "music generation with deep learning"
    - "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)