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: "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
  Abstract: Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance comparable to the models trained with twice more training data.

Connected through hyperedge HE-1 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language"):
  with members:
    - "how does learning rate decay help modern neural networks"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "tied multitask learning for neural speech translation"
    - "advancing acoustic to word ctc model"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages"):
  with members:
    - "learned in speech recognition contextual acoustic word embeddings"
    - "audio linguistic embeddings for spoken sentences"
    - "bayesian subspace hidden markov model for acoustic unit discovery"
    - "massively multilingual adversarial speech recognition"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"

Connected through hyperedge HE-3 (induced by source paper "multilingual end to end speech translation"):
  with members:
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "tied multitask learning for neural speech translation"
    - "transfer learning of language independent end to end asr with language model fusion"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-4 (induced by source paper "analyzing asr pretraining for low resource speech to text translation"):
  with members:
    - "fluent translations from disfluent speech in end to end speech translation"
    - "massively multilingual adversarial speech recognition"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"
    - "multilingual bottleneck features for subword modeling in zero resource languages"

Connected through hyperedge HE-5 (induced by source paper "meta learning for end to end low resource speech recognition"):
  with members:
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "massively multilingual adversarial speech recognition"
    - "on first order meta learning algorithms"
    - "espnet end to end speech processing toolkit"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "learning to adapt a meta learning approach for speaker adaptation"
    - "meta learning for low resource neural machine translation"

Connected through hyperedge HE-6 (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"
    - "multi modal data augmentation for end to end asr"
    - "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-7 (induced by source paper "transfer learning of language independent end to end asr with language model fusion"):
  with members:
    - "back translation style data augmentation for end to end asr"
    - "espnet end to end speech processing toolkit"
    - "sequence based multi lingual low resource speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "cold fusion training seq2seq models together with language models"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "unsupervised pretraining for sequence to sequence 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)