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: "a fully convolutional deep auditory model for musical chord recognition"
  Abstract: Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "polyphonic pitch tracking with deep layered learning"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "deep layered learning in mir"
    - "modeling majorness as a perceptual property in music from listener ratings"
    - "genre agnostic key classification with convolutional neural networks"
    - "end to end musical key estimation using a convolutional neural network"
    - "feature learning for chord recognition the deep chroma extractor"

Connected through hyperedge HE-2 (induced by source paper "a bi directional transformer for musical chord recognition"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved chord recognition by combining duration and harmonic language models"
    - "a large scale study of language models for chord prediction"
    - "attention is all you need"
    - "feature learning for chord recognition the deep chroma extractor"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (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"
    - "text based lstm networks for automatic music composition"
    - "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-4 (induced by source paper "using musical relationships between chord labels in automatic chord extraction tasks"):
  with members:
    - "feature learning for chord recognition the deep chroma extractor"

Connected through hyperedge HE-5 (induced by source paper "from context to concept exploring semantic relationships in music with word2vec"):
  with members:
    - "a functional taxonomy of music generation systems"
    - "from bach to the beatles the simulation of human tonal expectation using ecologically trained predictive models"
    - "convolutional neural networks for sentence classification"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "automatic chord recognition with higher order harmonic language modelling"):
  with members:
    - "a large scale study of language models for chord prediction"
    - "on the futility of learning complex frame level language models for chord recognition"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "feature learning for chord recognition the deep chroma extractor"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "improved chord recognition by combining duration and harmonic language models"):
  with members:
    - "a large scale study of language models for chord prediction"
    - "on the futility of learning complex frame level language models for chord recognition"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "feature learning for chord recognition the deep chroma extractor"
    - "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"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-8 (induced by source paper "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"):
  with members:
    - "convolutional recurrent neural networks for speech enhancement"
    - "multi scale multi band densenets for audio source separation"
    - "aenet learning deep audio features for video analysis"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "wide residual networks"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)