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: "advances in all neural speech recognition"
  Abstract: This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology.

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 "acoustic to word models with conversational context information"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "dialog context aware end to end speech recognition"
    - "multi modal data augmentation for end to end asr"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "english conversational telephone speech recognition by humans and machines"

Connected through hyperedge HE-3 (induced by source paper "gated embeddings in end to end speech recognition for conversational context fusion"):
  with members:
    - "advancing acoustic to word ctc model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved training of end to end attention models for speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "towards language universal end to end speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-4 (induced by source paper "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"):
  with members:
    - "improved training for online end to end speech recognition systems"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "simplified end to end mmi training and voting for asr"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-5 (induced by source paper "on using 2d sequence to sequence models for speech recognition"):
  with members:
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "end to end attention based large vocabulary speech recognition"
    - "neural machine translation of rare words with subword units"
    - "grid long short term memory"
    - "attention based models for speech recognition"

Connected through hyperedge HE-6 (induced by source paper "weighted automata extraction from recurrent neural networks via regression on state spaces"):
  with members:
    - "state regularized recurrent neural networks"
    - "intrusion prediction with system call sequence to sequence model"
    - "explaining black boxes on sequential data using weighted automata"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "bisimulation metrics for weighted automata"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "advancing acoustic to word ctc model with attention and mixed units"):
  with members:
    - "advancing acoustic to word ctc model"
    - "developing far field speaker system via teacher student learning"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-8 (induced by source paper "zero shot keyword spotting for visual speech recognition in the wild"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "espnet end to end speech processing toolkit"
    - "end to end audiovisual speech recognition"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"
    - "direct acoustics to word models for english conversational speech 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.CL(Computation and Language)