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: "generative choreography using deep learning"
  Abstract: Recent advances in deep learning have enabled the extraction of high-level features from raw sensor data which has opened up new possibilities in many different fields, including computer generated choreography. In this paper we present a system chor-rnn for generating novel choreographic material in the nuanced choreographic language and style of an individual choreographer. It also shows promising results in producing a higher level compositional cohesion, rather than just generating sequences of movement. At the core of chor-rnn is a deep recurrent neural network trained on raw motion capture data and that can generate new dance sequences for a solo dancer. Chor-rnn can be used for collaborative human-machine choreography or as a creative catalyst, serving as inspiration for a choreographer.

Connected through hyperedge HE-1 (induced by source paper "generative autoregressive networks for 3d dancing move synthesis from music"):
  with members:
    - "modeling human motion with quaternion based neural networks"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "text2action generative adversarial synthesis from language to action"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "machine learning for data driven movement generation a review of the state of the art"):
  with members:
    - "lstm a search space odyssey"
    - "on human motion prediction using recurrent neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "recurrent network models for human dynamics"

Connected through hyperedge HE-3 (induced by source paper "beyond imitation generative and variational choreography via machine learning"):
  with members:
    - "autoencoding beyond pixels using a learned similarity metric"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (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"
    - "text based lstm networks for automatic music composition"
    - "using the output embedding to improve language models"
    - "music transcription modelling and composition using deep learning"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised deep recurrent neural networks for basic dance step generation"):
  with members:
    - "weakly supervised hierarchical text classification"
    - "audio super resolution using neural networks"
    - "end to end musical key estimation using a convolutional neural network"
    - "madmom a new python audio and music signal processing library"
    - "audio event detection using weakly labeled data"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks"):
  with members:
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "generating sequences with recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "creative robot dance with variational encoder"):
  with members:
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-8 (induced by source paper "real time interactive sequence generation and control with recurrent neural network ensembles"):
  with members:
    - "lstm a search space odyssey"
    - "investigating gated recurrent neural networks for speech synthesis"
    - "deep learning in neural networks"
    - "draw a recurrent neural network for image generation"
    - "learning to execute"
    - "sequence to sequence learning with neural networks"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "generating sequences with recurrent neural networks"

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)