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: "escape a large scale synthetic corpus for automatic post editing"
  Abstract: Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors with the corresponding corrections made by a human post-editor. Ideally, a large amount of data of this kind should allow the model to learn reliable correction patterns and effectively apply them at test stage on unseen (source, MT) pairs. In practice, however, their limited availability calls for solutions that also integrate in the training process other sources of knowledge. Along this direction, state-of-the-art results have been recently achieved by systems that, in addition to a limited amount of available training data, exploit artificial corpora that approximate elements of the "gold" training instances with automatic translations. Following this idea, we present eSCAPE, the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. eSCAPE consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as an artificial human post-edit. Translations are obtained both with phrase-based and neural models. For each MT paradigm, eSCAPE contains 7.2 million triplets for English-German and 3.3 millions for English-Italian, resulting in a total of 14,4 and 6,6 million instances respectively. The usefulness of eSCAPE is proved through experiments in a general-domain scenario, the most challenging one for automatic post-editing. For both language directions, the models trained on our artificial data always improve MT quality with statistically significant gains. The current version of eSCAPE can be freely downloaded from: this http URL

Connected through hyperedge HE-1 (induced by source paper "learning to copy for automatic post editing"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "cross lingual language model pretraining"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "modeling localness for self attention networks"
    - "a shared attention mechanism for interpretation of neural automatic post editing systems"
    - "thumt an open source toolkit for neural machine translation"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-2 (induced by source paper "a simple and effective approach to automatic post editing with transfer learning"):
  with members:
    - "unbabel s submission to the wmt2019 ape shared task bert based encoder decoder for automatic post editing"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "marian fast neural machine translation in c"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-3 (induced by source paper "uds submission for the wmt 19 automatic post editing task"):
  with members:
    - "neutron an implementation of the transformer translation model and its variants"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "towards robust neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "improving the transformer translation model with document level context"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "unbabel s submission to the wmt2019 ape shared task bert based encoder decoder for automatic post editing"):
  with members:
    - "a simple and effective approach to automatic post editing with transfer learning"
    - "multi task deep neural networks for natural language understanding"
    - "unbabel s participation in the wmt19 translation quality estimation shared task"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "opennmt open source toolkit for neural machine translation"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-5 (induced by source paper "combining smt and nmt back translated data for efficient nmt"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "understanding back translation at scale"
    - "opennmt open source toolkit for neural machine translation"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "unbabel s participation in the wmt19 translation quality estimation shared task"):
  with members:
    - "a simple and effective approach to automatic post editing with transfer learning"
    - "openkiwi an open source framework for quality estimation"
    - "cross lingual language model pretraining"
    - "bilingual expert can find translation errors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "opennmt open source toolkit for neural machine translation"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-7 (induced by source paper "the transference architecture for automatic post editing"):
  with members:
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"
    - "pre translation for neural machine translation"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "attention is all you need"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "neural machine translation of rare words with subword units"

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)