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: "an empirical study on the effectiveness of images in multimodal neural machine translation"
  Abstract: In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effectiveness of the attention mechanism has also been explored for multimodal tasks, where it becomes possible to focus both on sentence parts and image regions that they describe. In this paper, we compare several attention mechanism on the multimodal translation task (English, image to German) and evaluate the ability of the model to make use of images to improve translation. We surpass state-of-the-art scores on the Multi30k data set, we nevertheless identify and report different misbehavior of the machine while translating.

Connected through hyperedge HE-1 (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-2 (induced by source paper "visual agreement regularized training for multi modal machine translation"):
  with members:
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "distilling translations with visual awareness"
    - "unsupervised multi modal neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "latent variable model for multi modal translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-3 (induced by source paper "bringing back simplicity and lightliness into neural image captioning"):
  with members:
    - "umons submission for wmt18 multimodal translation task"
    - "improved image captioning via policy gradient optimization of spider"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "attention is all you need"
    - "visually grounded word embeddings and richer visual features for improving multimodal neural machine translation"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "lium cvc submissions for wmt17 multimodal translation task"
    - "review networks for caption generation"

Connected through hyperedge HE-4 (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"
    - "does multimodality help human and machine for translation and image captioning"
    - "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-5 (induced by source paper "modulating and attending the source image during encoding improves multimodal translation"):
  with members:
    - "imagination improves multimodal translation"
    - "modulating early visual processing by language"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "multi30k multilingual english german image descriptions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

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)