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: "doubly attentive decoder for multi modal neural machine translation"
  Abstract: We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.

Connected through hyperedge HE-1 (induced by source paper "probing contextualized sentence representations with visual awareness"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "semantics aware bert for language understanding"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "sg net syntax guided machine reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "glyce glyph vectors for chinese character representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "debiasing word embeddings improves multimodal machine translation"):
  with members:
    - "multimodal machine translation with embedding prediction"
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "all but the top simple and effective postprocessing for word representations"

Connected through hyperedge HE-5 (induced by source paper "adversarial reconstruction for multi modal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "unsupervised image captioning"
    - "spectral normalization for generative adversarial networks"
    - "umons submission for wmt18 multimodal translation task"
    - "imagination improves multimodal translation"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "multimodal machine translation with embedding prediction"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "learning word vectors for 157 languages"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "all but the top simple and effective postprocessing for word representations"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-7 (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"
    - "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"
    - "neural machine translation with latent semantic of image and text"

Connected through hyperedge HE-8 (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"
    - "nematus a toolkit for neural machine translation"
    - "cuni system for wmt16 automatic post editing and multimodal translation tasks"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

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)