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: "joint training for neural machine translation models with monolingual data"
  Abstract: Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data. In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.

Connected through hyperedge HE-1 (induced by source paper "image captioning with very scarce supervised data adversarial semi supervised learning approach"):
  with members:
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "textual explanations for self driving vehicles"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "iterative learning with open set noisy labels"
    - "unpaired image captioning by language pivoting"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-2 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "investigating backtranslation in neural machine translation"
    - "multi task neural models for translating between styles within and across languages"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

Connected through hyperedge HE-3 (induced by source paper "acquiring knowledge from pre trained model to neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "learning representation mapping for relation detection in knowledge base question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "dynamic past and future for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "improving neural machine translation with pre trained representation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "explicit cross lingual pre training for unsupervised machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-6 (induced by source paper "iterative batch back translation for neural machine translation a conceptual model"):
  with members:
    - "tagged back translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "scaling neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised neural machine translation with smt as posterior regularization"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "ape at scale and its implications on mt evaluation biases"):
  with members:
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "self attention with relative position representations"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "the university of cambridge s machine translation systems for wmt18"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "a call for clarity in reporting bleu scores"
    - "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)