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: "does multimodality help human and machine for translation and image captioning"
  Abstract: This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal#R##N#data. We also performed a human evaluation in order to estimate the usefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.

Connected through hyperedge HE-1 (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"
    - "the memad submission to the wmt18 multimodal translation task"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "umons submission for wmt18 multimodal translation task"):
  with members:
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "an empirical study on the effectiveness of images in multimodal neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "search engine guided non parametric neural machine translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "predicting target language ccg supertags improves neural machine translation"
    - "billion scale similarity search with gpus"
    - "exploiting cross sentence context for neural machine translation"
    - "neural episodic control"
    - "learning to remember rare events"
    - "towards string to tree neural machine translation"
    - "task oriented query reformulation with reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "cuni system for wmt16 automatic post editing and multimodal translation tasks"
    - "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-5 (induced by source paper "multimodal compact bilinear pooling for multimodal neural machine translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "multi30k multilingual english german image descriptions"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "compact bilinear pooling"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"

Connected through hyperedge HE-6 (induced by source paper "zero resource translation with multi lingual neural machine translation"):
  with members:
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "character based neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

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)