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: "multi modal data augmentation for end to end asr"
  Abstract: We present a new end-to-end architecture for automatic speech recognition (ASR) that can be trained using \emph{symbolic} input in addition to the traditional acoustic input. This architecture utilizes two separate encoders: one for acoustic input and another for symbolic input, both sharing the attention and decoder parameters. We call this architecture a multi-modal data augmentation network (MMDA), as it can support multi-modal (acoustic and symbolic) input. The MMDA architecture attempts to eliminate the need for an external LM, by enabling seamless mixing of large text datasets with significantly smaller transcribed speech corpora during training. We study different ways of transforming large text corpora into a symbolic form suitable for training our MMDA network. Our best MMDA setup obtains small improvements on CER and achieves 8-10\% relative WER improvement on the WSJ data set.

Connected through hyperedge HE-1 (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"
    - "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"
    - "building competitive direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-2 (induced by source paper "independent language modeling architecture for end to end asr"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "espnet end to end speech processing toolkit"
    - "end to end speech recognition with word based rnn language models"
    - "catastrophic forgetting still a problem for dnns"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "state of the art speech recognition with sequence to sequence models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "a practical two stage training strategy for multi stream end to end speech recognition"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "end to end speech recognition with word based rnn language models"
    - "multi encoder multi resolution framework for end to end speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "regularizing neural networks by penalizing confident output distributions"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-4 (induced by source paper "leveraging weakly supervised data to improve end to end speech to text translation"):
  with members:
    - "back translation style data augmentation for end to end asr"
    - "tied multitask learning for neural speech translation"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "structured based curriculum learning for end to end english japanese speech translation"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "back translation style data augmentation for end to end asr"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "unsupervised machine translation using monolingual corpora only"
    - "listening while speaking speech chain by deep learning"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "cold fusion training seq2seq models together with language models"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "cycle consistency training for end to end speech recognition"):
  with members:
    - "back translation style data augmentation for end to end asr"
    - "espnet end to end speech processing toolkit"
    - "machine speech chain with one shot speaker adaptation"
    - "listening while speaking speech chain by deep learning"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "joint ctc attention based end to end speech recognition using multi task learning"

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)