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: "inducing grammars with and for neural machine translation"
  Abstract: Machine translation systems require semantic knowledge and grammatical understanding. Neural machine translation (NMT) systems often assume this information is captured by an attention mechanism and a decoder that ensures fluency. Recent work has shown that incorporating explicit syntax alleviates the burden of modeling both types of knowledge. However, requiring parses is expensive and does not explore the question of what syntax a model needs during translation. To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees. In this way, we leverage the benefits of structure while investigating what syntax NMT must induce to maximize performance. We show that our dependency trees are 1. language pair dependent and 2. improve translation quality.

Connected through hyperedge HE-1 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"

Connected through hyperedge HE-2 (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"
    - "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"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-3 (induced by source paper "inducing constituency trees through neural machine translation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "compound probabilistic context free grammars for grammar induction"
    - "an imitation learning approach to unsupervised parsing"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "linguistically informed self attention for semantic role labeling"
    - "grammar induction with neural language models an unusual replication"
    - "learning to parse and translate improves neural machine translation"
    - "towards neural machine translation with latent tree attention"

Connected through hyperedge HE-4 (induced by source paper "modeling latent sentence structure in neural machine translation"):
  with members:
    - "structured attention networks"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "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 "evaluating discourse in structured text representations"):
  with members:
    - "linguistically informed self attention for semantic role labeling"
    - "discourse structure in machine translation evaluation"
    - "learning structured text representations"
    - "neural discourse structure for text categorization"
    - "learning to compose task specific tree structures"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "learning to compose words into sentences with reinforcement learning"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-6 (induced by source paper "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"):
  with members:
    - "deep contextualized word representations"
    - "structured attention networks"
    - "learning structured text representations"
    - "neural probabilistic model for non projective mst parsing"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "crf autoencoder for unsupervised dependency parsing"

Connected through hyperedge HE-7 (induced by source paper "findings of the second workshop on neural machine translation and generation"):
  with members:
    - "modeling latent sentence structure in neural machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "towards one shot learning for rare word translation with external experts"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "multi source neural machine translation with missing data"
    - "marian cost effective high quality neural machine translation in c"
    - "extreme adaptation for personalized neural machine translation"
    - "bi directional neural machine translation with synthetic parallel 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)