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: "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
  Abstract: This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018. Based on training data and systems from the WMT2017 shared task, we re-implement our own models from the last shared task and introduce improvements based on extensive parameter sharing. Next we experiment with our implementation of dual-source transformer models and data selection for the IT domain. Our submissions decisively wins the SMT post-editing sub-task establishing the new state-of-the-art and is a very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on the rather weak results in the NMT sub-task, we hypothesize that neural-on-neural APE might not be actually useful.

Connected through hyperedge HE-1 (induced by source paper "incorporating bert into neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "distilling the knowledge of bert for text generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "document level neural machine translation with hierarchical attention networks"

Connected through hyperedge HE-2 (induced by source paper "automatic post editing for machine translation"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "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"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"

Connected through hyperedge HE-3 (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"
    - "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"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "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-6 (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"
    - "attention is all you need"

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"
    - "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-8 (induced by source paper "text repair model for neural machine translation"):
  with members:
    - "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"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "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)