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: "neural machine translation with latent semantic of image and text"
  Abstract: Although attention-based Neural Machine Translation have achieved great success, attention-mechanism cannot capture the entire meaning of the source sentence because the attention mechanism generates a target word depending heavily on the relevant parts of the source sentence. The report of earlier studies has introduced a latent variable to capture the entire meaning of sentence and achieved improvement on attention-based Neural Machine Translation. We follow this approach and we believe that the capturing meaning of sentence benefits from image information because human beings understand the meaning of language not only from textual information but also from perceptual information such as that gained from vision. As described herein, we propose a neural machine translation model that introduces a continuous latent variable containing an underlying semantic extracted from texts and images. Our model, which can be trained end-to-end, requires image information only when training. Experiments conducted with an English–German translation task show that our model outperforms over the baseline.

Connected through hyperedge HE-1 (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"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "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"

Connected through hyperedge HE-2 (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"
    - "incorporating global visual features into attention based neural machine translation"
    - "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-3 (induced by source paper "imagination improves multimodal translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "multi task video captioning with video and entailment generation"
    - "a correlational encoder decoder architecture for pivot based sequence generation"
    - "representation of linguistic form and function in recurrent neural networks"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "multimodal pivots for image caption translation"
    - "variational neural machine translation"
    - "fast domain adaptation for neural machine translation"

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)