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: "high dimensional sequence transduction"
  Abstract: We investigate the problem of transforming an input sequence into a high-dimensional output sequence in order to transcribe polyphonic audio music into symbolic notation. We introduce a probabilistic model based on a recurrent neural network that is able to learn realistic output distributions given the input and we devise an efficient algorithm to search for the global mode of that distribution. The resulting method produces musically plausible transcriptions even under high levels of noise and drastically outperforms previous state-of-the-art approaches on five datasets of synthesized sounds and real recordings, approximately halving the test error rate.

Connected through hyperedge HE-1 (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies"):
  with members:
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "look listen and learn"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "transfer learning for music classification and regression tasks"
    - "one model to learn them all"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "fma a dataset for music analysis"

Connected through hyperedge HE-2 (induced by source paper "this time with feeling learning expressive musical performance"):
  with members:
    - "morpheus generating structured music with constrained patterns and tension"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a functional taxonomy of music generation systems"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "getting closer to the essence of music the con espressione manifesto"
    - "neural translation of musical style"
    - "maximum entropy models for generation of expressive music"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "temporal pattern attention for multivariate time series forecasting"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "integrated pre processing for bayesian nonlinear system identification with gaussian processes"

Connected through hyperedge HE-4 (induced by source paper "linear memory networks"):
  with members:
    - "self normalizing neural networks"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "hierarchical multiscale recurrent neural networks"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "dance dance convolution"):
  with members:
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "character aware neural language models"
    - "visualizing and understanding recurrent networks"
    - "semi supervised sequence learning"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a siamese long short term memory architecture for human re identification"):
  with members:
    - "lstm a search space odyssey"
    - "transferring a semantic representation for person re identification and search"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "learning to rank in person re identification with metric ensembles"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-7 (induced by source paper "end to end attention based large vocabulary speech recognition"):
  with members:
    - "listen attend and spell"
    - "blocks and fuel frameworks for deep learning"
    - "show attend and tell neural image caption generation with visual attention"
    - "gated feedback recurrent neural networks"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "attention based models for speech recognition"
    - "on using monolingual corpora in neural machine translation"
    - "recurrent models of visual attention"

Connected through hyperedge HE-8 (induced by source paper "a hybrid recurrent neural network for music transcription"):
  with members:
    - "sequence transduction with recurrent neural networks"
    - "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.LG(Machine Learning)