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: "towards bidirectional hierarchical representations for attention based neural machine translation"
  Abstract: This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder. To maximize the predictive likelihood of target words, a weighted variant of an attention mechanism is used to balance the attentive information between lexical and phrase vectors. Using a tree-based rare word encoding, the proposed model is extended to sub-word level to alleviate the out-of-vocabulary (OOV) problem. Empirical results reveal that the proposed model significantly outperforms sequence-to-sequence attention-based and tree-based neural translation models in English-Chinese translation tasks.

Connected through hyperedge HE-1 (induced by source paper "tree structured attention with hierarchical accumulation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "tree transformer a transformer based method for correction of tree structured data"
    - "pay less attention with lightweight and dynamic convolutions"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "on tree based neural sentence modeling"
    - "self attention with relative position representations"
    - "scaling neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "assessing the ability of self attention networks to learn word order"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "modeling recurrence for transformer"
    - "towards robust neural machine translation"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-3 (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"
    - "tied multitask learning for neural speech 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"

Connected through hyperedge HE-4 (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"
    - "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"
    - "modeling past and future for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "dynamic compositionality in recursive neural networks with structure aware tag representations"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "enhancing sentence embedding with generalized pooling"
    - "learning to generate reviews and discovering sentiment"
    - "learned in translation contextualized word vectors"
    - "decoupled weight decay regularization"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "dynamic compositional neural networks over tree structure"

Connected through hyperedge HE-6 (induced by source paper "convolutional self attention network"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "modeling recurrence for transformer"
    - "exploiting deep representations for neural machine translation"
    - "group normalization"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "modeling localness for self attention networks"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "multi head attention with disagreement regularization"
    - "accelerating neural transformer via an average attention network"
    - "self attentional acoustic models"

Connected through hyperedge HE-8 (induced by source paper "neural machine translation with source side latent graph parsing"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "tree to sequence attentional neural machine translation"
    - "enriching word vectors with subword information"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "dependency parsing as head selection"
    - "using the output embedding to improve language models"

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)