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: "improved neural machine translation with a syntax aware encoder and decoder"
  Abstract: Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.

Connected through hyperedge HE-1 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks"):
  with members:
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "a critical analysis of biased parsers in unsupervised parsing"
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "tree to tree neural networks for program translation"

Connected through hyperedge HE-3 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "tied multitask learning for neural speech 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"

Connected through hyperedge HE-5 (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"
    - "neural machine translation with source side latent graph parsing"
    - "chunk based bi scale decoder for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "saliency driven word alignment interpretation for neural machine translation"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "methods for interpreting and understanding deep neural networks"
    - "neural machine translation decoding with terminology constraints"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "generating sentences from disentangled syntactic and semantic spaces"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "bag of words as target for neural machine translation"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "syntax directed variational autoencoder for structured data"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"

Connected through hyperedge HE-8 (induced by source paper "is word segmentation necessary for deep learning of chinese representations"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bag of words as target for neural machine translation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "modeling source syntax for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"
    - "which encoding is the best for text classification in chinese english japanese and korean"

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)