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 the wmt18 multimodal translation task"
  Abstract: We present our submission to the WMT18 Multimodal Translation Task. The main feature of our submission is applying a self-attentive network instead of a recurrent neural network. We evaluate two methods of incorporating the visual features in the model: first, we include the image representation as another input to the network; second, we train the model to predict the visual features and use it as an auxiliary objective. For our submission, we acquired both textual and multimodal additional data. Both of the proposed methods yield significant improvements over recurrent networks and self-attentive textual baselines.

Connected through hyperedge HE-1 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "context aware self attention networks"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"

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"
    - "the memad submission to the wmt18 multimodal translation task"

Connected through hyperedge HE-3 (induced by source paper "probing representations learned by multimodal recurrent and transformer models"):
  with members:
    - "input combination strategies for multi source transformer decoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable textual neuron representations for nlp"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "imagination improves multimodal translation"
    - "cuni system for the wmt17 multimodal translation task"

Connected through hyperedge HE-4 (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"
    - "the memad submission to 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-5 (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"
    - "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"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "all but the top simple and effective postprocessing for word representations"

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"
    - "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"
    - "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 "multimodal machine translation with embedding prediction"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "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"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "all but the top simple and effective postprocessing for word representations"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-8 (induced by source paper "predicting actions to help predict translations"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "distilling translations with visual awareness"
    - "tensor2tensor for neural machine translation"
    - "training tips for the transformer model"
    - "how2 a large scale dataset for multimodal language understanding"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "multi30k multilingual english german image descriptions"

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)