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: "estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks"
  Abstract: In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.

Connected through hyperedge HE-1 (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-2 (induced by source paper "detection of glottal closure instants from raw speech using convolutional neural networks"):
  with members:
    - "glottal closure and opening instant detection from speech signals"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "glottal closure instants detection from pathological acoustic speech signal using deep learning"
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "detection of glottal closure instants using deep dilated convolutional neural networks"):
  with members:
    - "glottal closure and opening instant detection from speech signals"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "glottal closure instants detection from pathological acoustic speech signal using deep learning"
    - "noisy activation functions"
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural networks for predominant instrument recognition in polyphonic music"):
  with members:
    - "understanding neural networks through deep visualization"
    - "recent advances in convolutional neural networks"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "learning filterbanks from raw speech for phone recognition"):
  with members:
    - "wav2letter an end to end convnet based speech recognition system"
    - "attention based wav2text with feature transfer learning"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "wavenet a generative model for raw audio"
    - "achieving human parity in conversational speech recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "wav2letter an end to end convnet based speech recognition system"):
  with members:
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "the ibm 2015 english conversational telephone speech recognition system"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "end to end learning for 3d facial animation from raw waveforms of speech"):
  with members:
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "fast chirplet transform to enhance cnn machine listening validation on animal calls and speech"):
  with members:
    - "deep scattering spectrum"
    - "invariant scattering convolution 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.LG(Machine Learning)