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: "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
  Abstract: We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription.

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 "temporally folded convolutional neural networks for sequence forecasting"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "stcn stochastic temporal convolutional networks"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "recurrent convolutions for causal 3d cnns"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "convolutional image captioning"

Connected through hyperedge HE-3 (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"
    - "text based lstm networks for automatic music composition"
    - "deepbach a steerable model for bach chorales 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 "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"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "c rnn gan continuous recurrent neural networks with adversarial training"

Connected through hyperedge HE-6 (induced by source paper "the impact of audio input representations on neural network based music transcription"):
  with members:
    - "onsets and frames dual objective piano transcription"
    - "deep complex networks"
    - "on the potential of simple framewise approaches to piano transcription"

Connected through hyperedge HE-7 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "r transformer recurrent neural network enhanced transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "convolutional sequence to sequence learning"

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.LG(Machine Learning)