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: "multilingual image description with neural sequence models"
  Abstract: In this paper we present an approach to multi-language image description bringing together insights from neural machine translation and neural image description. To create a description of an image for a given target language, our sequence generation models condition on feature vectors from the image, the description from the source language, and/or a multimodal vector computed over the image and a description in the source language. In image description experiments on the IAPR-TC12 dataset of images aligned with English and German sentences, we find significant and substantial improvements in BLEU4 and Meteor scores for models trained over multiple languages, compared to a monolingual baseline.

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 "improving captioning for low resource languages by cycle consistency"):
  with members:
    - "neural baby talk"
    - "unpaired image captioning by language pivoting"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "multi30k multilingual english german image descriptions"
    - "self critical sequence training for image captioning"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "doubly attentive transformer machine translation"):
  with members:
    - "image transformer"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "attention is all you need"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "nematus a toolkit for neural machine translation"
    - "cuni system for wmt16 automatic post editing and multimodal translation tasks"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"

Connected through hyperedge HE-4 (induced by source paper "multimodal named entity recognition for short social media posts"):
  with members:
    - "a multi task approach for named entity recognition in social media data"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "attending to characters in neural sequence labeling models"
    - "end to end memory networks"
    - "listen attend and spell"
    - "vqa visual question answering"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-5 (induced by source paper "input combination strategies for multi source transformer decoder"):
  with members:
    - "context aware neural machine translation learns anaphora resolution"
    - "multi source neural machine translation with missing data"
    - "imagination improves multimodal translation"
    - "attention is all you need"
    - "one model to learn them all"
    - "enabling multi source neural machine translation by concatenating source sentences in multiple languages"
    - "using the output embedding to improve language models"
    - "multi source neural translation"

Connected through hyperedge HE-6 (induced by source paper "imagination improves multimodal translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "neural machine translation with latent semantic of image and text"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "fluency guided cross lingual image captioning"):
  with members:
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "boosting image captioning with attributes"
    - "self critical sequence training for image captioning"
    - "yfcc100m the new data in multimedia research"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "image captioning with deep bidirectional lstms"

Connected through hyperedge HE-8 (induced by source paper "findings of the second shared task on multimodal machine translation and multilingual image description"):
  with members:
    - "cross linguistic differences and similarities in image descriptions"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "osu multimodal machine translation system report"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "multimodal pivots for image caption translation"
    - "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)