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: "tied multitask learning for neural speech translation"
  Abstract: We explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information. Second, we apply regularization that encourages transitivity and invertibility. We show that the application of these notions on jointly trained models improves performance on the tasks of low-resource speech transcription and translation. It also leads to better performance when using attention information for word discovery over unsegmented input.

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"
    - "advancing acoustic to word ctc model"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "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 "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "recurrent graph syntax encoder for neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "semantic neural machine translation using amr"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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-5 (induced by source paper "one to many multilingual end to end speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "end to end speech translation with knowledge distillation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "deep neural machine translation with weakly recurrent units"
    - "leveraging weakly supervised data to improve end to end speech to text translation"
    - "structured based curriculum learning for end to end english japanese speech translation"

Connected through hyperedge HE-6 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a comparative study on end to end speech to text translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "on using 2d sequence to sequence models for speech recognition"
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "low resource speech to text translation"
    - "end to end automatic speech translation of audiobooks"

Connected through hyperedge HE-8 (induced by source paper "dynamic layer aggregation for neural machine translation with routing by agreement"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

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)