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: "neural machine translation with source side latent graph parsing"
  Abstract: This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.

Connected through hyperedge HE-1 (induced by source paper "syntactically look ahead attention network for sentence compression"):
  with members:
    - "a unified linear time framework for sentence level discourse parsing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dependency parsing as head selection"
    - "improving sentence compression by learning to predict gaze"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations"):
  with members:
    - "towards robust neural machine translation"
    - "deep contextualized word representations"
    - "structural attention neural networks for improved sentiment analysis"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "attention is all you need"
    - "chunk based bi scale decoder for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"
    - "modeling past and future for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "improving neural machine translation with parent scaled self attention"):
  with members:
    - "context aware self attention networks"
    - "modeling latent sentence structure in neural machine translation"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "inducing grammars with and for neural machine translation"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-4 (induced by source paper "modeling latent sentence structure in neural machine translation"):
  with members:
    - "inducing grammars with and for neural machine translation"
    - "structured attention networks"
    - "attention is all you need"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "tree to sequence attentional neural machine translation"
    - "learning to compose words into sentences with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "accelerated reinforcement learning for sentence generation by vocabulary prediction"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning to parse and translate improves neural machine translation"
    - "neural machine translation with word predictions"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "neural machine translation via binary code prediction"
    - "a deep reinforced model for abstractive summarization"
    - "tree to sequence attentional neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "inducing grammars with and for neural machine translation"):
  with members:
    - "structured attention networks"
    - "learning to parse and translate improves neural machine translation"
    - "learning structured text representations"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "towards neural machine translation with latent tree attention"
    - "stronger baselines for trustable results in neural machine translation"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "graph to sequence learning using gated graph neural networks"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "neural amr sequence to sequence models for parsing and generation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "towards string to tree neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "amr to text generation with synchronous node replacement grammar"

Connected through hyperedge HE-8 (induced by source paper "multi source syntactic neural machine translation"):
  with members:
    - "predicting target language ccg supertags improves neural machine translation"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "tree to sequence attentional neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "improving neural machine translation models with monolingual data"

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)