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: "incorporating global visual features into attention based neural machine translation"
  Abstract: We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. We utilise global image features extracted using a pre-trained convolutional neural network and incorporate them (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate how these different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.

Connected through hyperedge HE-1 (induced by source paper "deep learning under privileged information using heteroscedastic dropout"):
  with members:
    - "imagination improves multimodal translation"
    - "opennmt open source toolkit for neural machine translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "neural machine translation with latent semantic of image and text"
    - "representation of linguistic form and function in recurrent neural networks"
    - "multimodal pivots for image caption translation"
    - "deep residual learning for image recognition"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "lium cvc submissions for wmt17 multimodal translation task"):
  with members:
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "attention strategies for multi source sequence to sequence learning"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "exploiting cross sentence context for neural machine translation"):
  with members:
    - "nematus a toolkit for neural machine translation"
    - "context dependent word representation for neural machine translation"
    - "neural machine translation with reconstruction"
    - "modeling coverage for neural machine translation"
    - "multi source neural translation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "effective approaches to attention based neural machine translation"
    - "a neural conversational model"

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)