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: "stair captions constructing a large scale japanese image caption dataset"
  Abstract: In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.

Connected through hyperedge HE-1 (induced by source paper "captioning images taken by people who are blind"):
  with members:
    - "attention on attention for image captioning"
    - "nocaps novel object captioning at scale"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "a comprehensive survey of deep learning for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "engaging image captioning via personality"
    - "speech coco 600k visually grounded spoken captions aligned to mscoco data set"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "aligning multilingual word embeddings for cross modal retrieval task"):
  with members:
    - "neural baby talk"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "learning two branch neural networks for image text matching tasks"
    - "multilingual multi modal embeddings for natural language processing"
    - "image pivoting for learning multilingual multimodal representations"
    - "word translation without parallel data"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "multimodal pivots for image caption translation"

Connected through hyperedge HE-3 (induced by source paper "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"):
  with members:
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "learning word like units from joint audio visual analysis"
    - "speech coco 600k visually grounded spoken captions aligned to mscoco data set"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "representations of language in a model of visually grounded speech signal"

Connected through hyperedge HE-4 (induced by source paper "unsupervised bilingual lexicon induction from mono lingual multimodal data"):
  with members:
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "multi30k multilingual english german image descriptions"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "microsoft coco captions data collection and evaluation server"
    - "visualizing and understanding convolutional networks"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-5 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "causally regularized learning with agnostic data selection bias"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-6 (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"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-7 (induced by source paper "image pivoting for learning multilingual multimodal representations"):
  with members:
    - "multilingual multi modal embeddings for natural language processing"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "zero resource translation with multi lingual neural machine translation"
    - "leveraging visual question answering for image caption ranking"
    - "multimodal pivots for image caption translation"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "correlational neural networks"
    - "bridge correlational neural networks for multilingual multimodal representation learning"

Connected through hyperedge HE-8 (induced by source paper "cross linguistic differences and similarities in image descriptions"):
  with members:
    - "pragmatic factors in image description the case of negations"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "seeing through the human reporting bias visual classifiers from noisy human centric labels"
    - "multilingual image description with neural sequence models"
    - "bridge correlational neural networks for multilingual multimodal representation learning"
    - "microsoft coco common objects in context"

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)