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: "sequence based multi lingual low resource speech recognition"
  Abstract: Techniques for multi-lingual and cross-lingual speech recognition can help in low resource scenarios, to bootstrap systems and enable analysis of new languages and domains. End-to-end approaches, in particular sequence-based techniques, are attractive because of their simplicity and elegance. While it is possible to integrate traditional multi-lingual bottleneck feature extractors as front-ends, we show that end-to-end multi-lingual training of sequence models is effective on context independent models trained using Connectionist Temporal Classification (CTC) loss. We show that our model improves performance on Babel languages by over 6% absolute in terms of word/phoneme error rate when compared to mono-lingual systems built in the same setting for these languages. We also show that the trained model can be adapted cross-lingually to an unseen language using just 25% of the target data. We show that training on multiple languages is important for very low resource cross-lingual target scenarios, but not for multi-lingual testing scenarios. Here, it appears beneficial to include large well prepared datasets.

Connected through hyperedge HE-1 (induced by source paper "towards zero shot learning for automatic phonemic transcription"):
  with members:
    - "multilingual speech recognition with corpus relatedness sampling"
    - "multilingual bottleneck features for subword modeling in zero resource languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-2 (induced by source paper "universal phone recognition with a multilingual allophone system"):
  with members:
    - "towards zero shot learning for automatic phonemic transcription"
    - "multilingual speech recognition with corpus relatedness sampling"
    - "towards a general purpose linguistic annotation backend"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "thchs 30 a free chinese speech corpus"

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"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "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"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "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"
    - "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"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"

Connected through hyperedge HE-6 (induced by source paper "phoneme level language models for sequence based low resource asr"):
  with members:
    - "domain robust feature extraction for rapid low resource asr development"
    - "comparison of decoding strategies for ctc acoustic models"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "enriching word vectors with subword information"
    - "character level incremental speech recognition with recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"

Connected through hyperedge HE-7 (induced by source paper "multilingual speech recognition with corpus relatedness sampling"):
  with members:
    - "domain robust feature extraction for rapid low resource asr development"
    - "enriching word vectors with subword information"
    - "a curriculum learning method for improved noise robustness in automatic speech recognition"
    - "achieving human parity in conversational speech recognition"
    - "character aware neural language models"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "thchs 30 a free chinese speech corpus"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "the ariel cmu systems for lorehlt18"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "domain robust feature extraction for rapid low resource asr development"
    - "enriching word vectors with subword information"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "bucking the trend large scale cost focused active learning for statistical machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)