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: "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
  Abstract: This paper describes the submission of the AMU (Adam Mickiewicz University) team to the Automatic Post-Editing (APE) task of WMT 2016. We explore the application of neural translation models to the APE problem and achieve good results by treating different models as components in a log-linear model, allowing for multiple inputs (the MT-output and the source) that are decoded to the same target language (post-edited translations). A simple string-matching penalty integrated within the log-linear model is used to control for higher faithfulness with regard to the raw machine translation output. To overcome the problem of too little training data, we generate large amounts of artificial data. Our submission improves over the uncorrected baseline on the unseen test set by -3.2\% TER and +5.5\% BLEU and outperforms any other system submitted to the shared-task by a large margin.

Connected through hyperedge HE-1 (induced by source paper "automatic post editing for machine translation"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "convolutional sequence to sequence learning"
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "pre translation for neural machine translation"
    - "systran s pure neural machine translation systems"

Connected through hyperedge HE-2 (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"
    - "joint training for neural machine translation models with monolingual data"

Connected through hyperedge HE-3 (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"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "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"

Connected through hyperedge HE-4 (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"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "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"

Connected through hyperedge HE-5 (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"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "context aware monolingual repair for neural machine translation"):
  with members:
    - "ape at scale and its implications on mt evaluation biases"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "achieving human parity on automatic chinese to english news translation"
    - "modeling coherence for discourse neural machine translation"
    - "training tips for the transformer model"
    - "context aware neural machine translation learns anaphora resolution"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "neural machine translation with extended context"

Connected through hyperedge HE-7 (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"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "text repair model for neural machine translation"):
  with members:
    - "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"
    - "explaining and generalizing back translation through wake sleep"
    - "phrase based neural unsupervised machine translation"
    - "edinburgh neural machine translation systems for wmt 16"
    - "improving neural machine translation models with monolingual 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)