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: "feature learning for chord recognition the deep chroma extractor"
  Abstract: We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. #R##N#We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.

Connected through hyperedge HE-1 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "dancing to music"
    - "learning individual styles of conversational gesture"
    - "a neural temporal model for human motion prediction"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "convolutional sequence to sequence model for human dynamics"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-2 (induced by source paper "music2dance music driven dance generation using wavenet"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"
    - "on human motion prediction using recurrent neural networks"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"

Connected through hyperedge HE-3 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-4 (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"
    - "a fully convolutional deep auditory model for musical chord recognition"

Connected through hyperedge HE-5 (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"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "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-6 (induced by source paper "using musical relationships between chord labels in automatic chord extraction tasks"):
  with members:
    - "a fully convolutional deep auditory model for musical chord recognition"

Connected through hyperedge HE-7 (induced by source paper "lead sheet generation and arrangement by conditional generative adversarial network"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "semi recurrent cnn based vae gan for sequential data generation"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "chord generation from symbolic melody using blstm networks"
    - "sampling variations of lead sheets"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (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"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "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"

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.SD(Sound)