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: "end to end speech recognition from the raw waveform"
  Abstract: State-of-the-art speech recognition systems rely on fixed, hand-crafted features such as mel-filterbanks to preprocess the waveform before the training pipeline. In this paper, we study end-to-end systems trained directly from the raw waveform, building on two alternatives for trainable replacements of mel-filterbanks that use a convolutional architecture. The first one is inspired by gammatone filterbanks (Hoshen et al., 2015; Sainath et al, 2015), and the second one by the scattering transform (Zeghidour et al., 2017). We propose two modifications to these architectures and systematically compare them to mel-filterbanks, on the Wall Street Journal dataset. The first modification is the addition of an instance normalization layer, which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks. The second one relates to the low-pass filter used in these approaches. These modifications consistently improve performances for both approaches, and remove the need for a careful initialization in scattering-based trainable filterbanks. In particular, we show a consistent improvement in word error rate of the trainable filterbanks relatively to comparable mel-filterbanks. It is the first time end-to-end models trained from the raw signal significantly outperform mel-filterbanks on a large vocabulary task under clean recording conditions.

Connected through hyperedge HE-1 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "espnet end to end speech processing toolkit"
    - "improving speech recognition by revising gated recurrent units"
    - "deep complex networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "end to end phoneme sequence recognition using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "single channel speech enhancement using temporal convolutional recurrent neural networks"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "supervised speech separation based on deep learning an overview"
    - "a wavenet for speech denoising"
    - "wavenet a generative model for raw audio"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "universal adversarial perturbations for speech recognition systems"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "adversarial initialization when your network performs the way i want"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "unsupervised text style transfer using language models as discriminators"

Connected through hyperedge HE-4 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset"):
  with members:
    - "learning to separate object sounds by watching unlabeled video"
    - "audio based disambiguation of music genre tags"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "the microsoft 2017 conversational speech recognition system"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio classification"
    - "multi label music genre classification from audio text and images using deep features"

Connected through hyperedge HE-5 (induced by source paper "end to end environmental sound classification using a 1d convolutional neural network"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "very deep convolutional neural networks for raw waveforms"
    - "cnn architectures for large scale audio classification"
    - "automatic environmental sound recognition performance versus computational cost"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-6 (induced by source paper "towards using context dependent symbols in ctc without state tying decision trees"):
  with members:
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep triphone embedding improves phoneme recognition"
    - "listen attend and spell"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "exploring spectro temporal features in end to end convolutional neural networks"):
  with members:
    - "improving speech recognition by revising gated recurrent units"
    - "learning filterbanks from raw speech for phone recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"
    - "deep scattering spectrum"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "fully convolutional speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "language modeling with gated convolutional networks"
    - "wavenet a generative model for raw audio"

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.CL(Computation and Language)