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: "the memad submission to the wmt18 multimodal translation task"
  Abstract: This paper describes the MeMAD project entry to the WMT Multimodal Machine Translation Shared Task. #R##N#We propose adapting the Transformer neural machine translation (NMT) architecture to a multi-modal setting. In this paper, we also describe the preliminary experiments with text-only translation systems leading us up to this choice. #R##N#We have the top scoring system for both English-to-German and English-to-French, according to the automatic metrics for flickr18. #R##N#Our experiments show that the effect of the visual features in our system is small. Our largest gains come from the quality of the underlying text-only NMT system. We find that appropriate use of additional data is effective.

Connected through hyperedge HE-1 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "grounded textual entailment"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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"
    - "how2 a large scale dataset for multimodal language understanding"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning to remember translation history with a continuous cache"

Connected through hyperedge HE-5 (induced by source paper "transformer based cascaded multimodal speech translation"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "predicting actions to help predict translations"
    - "distilling translations with visual awareness"
    - "achieving human parity on automatic chinese to english news translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cuni system for the wmt18 multimodal translation task"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-6 (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"
    - "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"
    - "osu multimodal machine translation system report"
    - "lium cvc submissions for wmt17 multimodal translation task"
    - "multimodal attention for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "dynamic fusion for multimodal data"):
  with members:
    - "multi lingual common semantic space construction via cluster consistent word embedding"
    - "multimodal speech emotion recognition using audio and text"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "how2 a large scale dataset for multimodal language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"

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)