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: "prior knowledge integration for neural machine translation using posterior regularization"
  Abstract: Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning process of the neural translation model. Experiments on Chinese-English translation show that our approach leads to significant improvements.

Connected through hyperedge HE-1 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "neural semantic parsing in low resource settings with back translation and meta learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "question generation from sql queries improves neural semantic parsing"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "how to train your maml"

Connected through hyperedge HE-3 (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"
    - "bag of words as target for neural machine translation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "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"

Connected through hyperedge HE-4 (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"
    - "joint training for neural machine translation models with monolingual data"
    - "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-5 (induced by source paper "merging external bilingual pairs into neural machine translation"):
  with members:
    - "code switching for enhancing nmt with pre specified translation"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "translating phrases in neural machine translation"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "unsupervised neural machine translation"
    - "neural machine translation leveraging phrase based models in a hybrid search"

Connected through hyperedge HE-6 (induced by source paper "big mood relating transformers to explicit commonsense knowledge"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"

Connected through hyperedge HE-7 (induced by source paper "neural chinese word segmentation with lexicon and unlabeled data via posterior regularization"):
  with members:
    - "neural chinese word segmentation with dictionary knowledge"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "transfer deep learning for low resource chinese word segmentation with a novel neural network"
    - "multi task domain adaptation for sequence tagging"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "explicit state tracking with semi supervisionfor neural dialogue generation"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "latent variable dialogue models and their diversity"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)