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: "cuni system for wmt16 automatic post editing and multimodal translation tasks"
  Abstract: Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.

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"
    - "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-2 (induced by source paper "latent variable model for multi modal translation"):
  with members:
    - "auto encoding variational neural machine translation"
    - "generative neural machine translation"
    - "imagination improves multimodal translation"
    - "opennmt open source toolkit for neural machine translation"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "doubly attentive transformer machine translation"):
  with members:
    - "image transformer"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "attention is all you need"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "nematus a toolkit for neural machine translation"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-4 (induced by source paper "cuni system for the wmt18 multimodal translation task"):
  with members:
    - "cuni system for the wmt17 multimodal translation task"
    - "attention is all you need"
    - "attention strategies for multi source sequence to sequence learning"
    - "identity mappings in deep residual networks"
    - "using the output embedding to improve language models"
    - "improving neural machine translation models with monolingual data"
    - "show attend and tell neural image caption generation with visual attention"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"

Connected through hyperedge HE-5 (induced by source paper "visually grounded word embeddings and richer visual features for improving multimodal neural machine translation"):
  with members:
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "does multimodality help human and machine for translation and image captioning"
    - "multi30k multilingual english german image descriptions"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "semantic code repair using neuro symbolic transformation networks"):
  with members:
    - "learning python code suggestion with a sparse pointer network"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "sentence level grammatical error identification as sequence to sequence correction"
    - "learning to superoptimize programs"
    - "a convolutional attention network for extreme summarization of source code"
    - "neural module networks"
    - "neural machine translation by jointly learning to align and translate"
    - "automated feedback generation for introductory programming assignments"

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)