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: "agreement based joint training for bidirectional attention based neural machine translation"
  Abstract: The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently,our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on Chinese-English and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.

Connected through hyperedge HE-1 (induced by source paper "shared private bilingual word embeddings for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "slim embedding layers for recurrent neural language models"

Connected through hyperedge HE-2 (induced by source paper "improving bidirectional decoding with dynamic target semantics in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "synchronous bidirectional inference for neural sequence generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "neural machine translation with key value memory augmented attention"

Connected through hyperedge HE-3 (induced by source paper "visual agreement regularized training for multi modal machine translation"):
  with members:
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "distilling translations with visual awareness"
    - "unsupervised multi modal neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "latent variable model for multi modal translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-4 (induced by source paper "language independent representor for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "joint training for neural machine translation models with monolingual data"
    - "data augmentation for low resource neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "improving lexical choice in neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "an operation sequence model for explainable neural machine translation"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "training tips for the transformer model"
    - "neural machine translation decoding with terminology constraints"
    - "qcri machine translation systems for iwslt 16"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"

Connected through hyperedge HE-6 (induced by source paper "variational recurrent neural machine translation"):
  with members:
    - "a gru gated attention model for neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "deep neural machine translation with linear associative unit"
    - "memory enhanced decoder for neural machine translation"
    - "variational neural machine translation"
    - "interactive attention for neural machine translation"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a multi task learning approach for improving product title compression with user search log data"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "neural summarization by extracting sentences and words"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "improving sentence compression by learning to predict gaze"
    - "language as a latent variable discrete generative models for sentence compression"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "multi head attention with disagreement regularization"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "thumt an open source toolkit for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"

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)