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: "osu multimodal machine translation system report"
  Abstract: This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I". In this task, all the sentence pairs are image captions in different languages. The key difference between this task and conventional machine translation is that we have corresponding images as additional information for each sentence pair. In this paper, we introduce a simple but effective system which takes an image shared between different languages, feeding it into the both encoding and decoding side. We report our system's performance for English-French and English-German with Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves the best performance in TER for English-German for MSCOCO dataset.

Connected through hyperedge HE-1 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "video description a survey of methods datasets and evaluation metrics"
    - "nocaps novel object captioning at scale"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"

Connected through hyperedge HE-2 (induced by source paper "context aware learning for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "improving the transformer translation model with document level context"
    - "document level neural machine translation with hierarchical attention networks"
    - "the memad submission to the wmt18 multimodal translation task"
    - "context aware neural machine translation learns anaphora resolution"
    - "learning to discriminate noises for incorporating external information in neural machine translation"
    - "extreme adaptation for personalized neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "probing the need for visual context in multimodal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "attention strategies for multi source sequence to sequence learning"
    - "lium cvc submissions for wmt17 multimodal translation task"
    - "multimodal attention for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "opennmt neural machine translation toolkit"):
  with members:
    - "structured attention networks"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "effective strategies in zero shot neural machine translation"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "challenges in data to document generation"

Connected through hyperedge HE-5 (induced by source paper "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"):
  with members:
    - "multi reference training with pseudo references for neural translation and text generation"
    - "classify or select neural architectures for extractive document summarization"
    - "improved image captioning via policy gradient optimization of spider"
    - "neural system combination for machine translation"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "multi30k multilingual english german image descriptions"
    - "self critical sequence training for image captioning"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-6 (induced by source paper "a visual attention grounding neural model for multimodal machine translation"):
  with members:
    - "imagination improves multimodal translation"
    - "cuni system for the wmt17 multimodal translation task"
    - "multilingual multi modal embeddings for natural language processing"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "image pivoting for learning multilingual multimodal representations"
    - "neural machine translation of rare words with subword units"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "findings of the second shared task on multimodal machine translation and multilingual image description"):
  with members:
    - "cross linguistic differences and similarities in image descriptions"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "edinburgh neural machine translation systems for wmt 16"

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)