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: "identifying semantic divergences in parallel text without annotations"
  Abstract: Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.

Connected through hyperedge HE-1 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "self supervised generalisation with meta auxiliary learning"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"

Connected through hyperedge HE-2 (induced by source paper "controlling neural machine translation formality with synthetic supervision"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "multi task neural models for translating between styles within and across languages"
    - "the missing ingredient in zero shot neural machine translation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "contextual parameter generation for universal neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "monolingual and cross lingual zero shot style transfer"
    - "sockeye a toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "handling divergent reference texts when evaluating table to text generation"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "towards a better metric for evaluating question generation systems"
    - "object hallucination in image captioning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-4 (induced by source paper "towards recognizing phrase translation processes experiments on english french"):
  with members:
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enriching word vectors with subword information"
    - "neural network based word alignment through score aggregation"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "a universal part of speech tagset"

Connected through hyperedge HE-5 (induced by source paper "multi task neural models for translating between styles within and across languages"):
  with members:
    - "style transfer through back translation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "extreme adaptation for personalized neural machine translation"
    - "bi directional neural machine translation with synthetic parallel data"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "sockeye a toolkit for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "denoising neural machine translation training with trusted data and online data selection"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "neural machine translation training in a multi domain scenario"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)