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: "watch what you just said image captioning with text conditional attention"
  Abstract: Attention mechanisms have attracted considerable interest in image captioning due to its powerful performance. However, existing methods use only visual content as attention and whether textual context can improve attention in image captioning remains unsolved. To explore this problem, we propose a novel attention mechanism, called \textit{text-conditional attention}, which allows the caption generator to focus on certain image features given previously generated text. To obtain text-related image features for our attention model, we adopt the guiding Long Short-Term Memory (gLSTM) captioning architecture with CNN fine-tuning. Our proposed method allows joint learning of the image embedding, text embedding, text-conditional attention and language model with one network architecture in an end-to-end manner. We perform extensive experiments on the MS-COCO dataset. The experimental results show that our method outperforms state-of-the-art captioning methods on various quantitative metrics as well as in human evaluation, which supports the use of our text-conditional attention in image captioning.

Connected through hyperedge HE-1 (induced by source paper "learning to generate grounded image captions without localization supervision"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "cycle consistency for robust visual question answering"
    - "grounding visual explanations"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "turbo learning for captionbot and drawingbot"
    - "neural baby talk"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "weakly supervised dense event captioning in videos"

Connected through hyperedge HE-2 (induced by source paper "image captioning based on deep learning methods a survey"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "learning to guide decoding for image captioning"
    - "rethinking the form of latent states in image captioning"
    - "exploring visual relationship for image captioning"
    - "mat a multimodal attentive translator for image captioning"
    - "convolutional image captioning"
    - "review networks for caption generation"
    - "image captioning with semantic attention"

Connected through hyperedge HE-3 (induced by source paper "image based review text generation with emotional guidance"):
  with members:
    - "image captioning with semantic attention"
    - "show attend and tell neural image caption generation with visual attention"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "recurrent fusion network for image captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-5 (induced by source paper "lucss language based user customized colourization of scene sketches"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketchyscene richly annotated scene sketches"
    - "texturegan controlling deep image synthesis with texture patches"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "outline colorization through tandem adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "grounded video description"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "move forward and tell a progressive generator of video descriptions"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"

Connected through hyperedge HE-7 (induced by source paper "end to end dense video captioning with masked transformer"):
  with members:
    - "activitynet challenge 2017 summary"
    - "attention is all you need"
    - "dense captioning events in videos"
    - "a structured self attentive sentence embedding"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "towards automatic learning of procedures from web instructional videos"
    - "a deep reinforced model for abstractive summarization"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-8 (induced by source paper "learning to guide decoding for image captioning"):
  with members:
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "text guided attention model for image captioning"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "an empirical study of language cnn for image captioning"
    - "recurrent image captioner describing images with spatial invariant transformation and attention filtering"
    - "self critical sequence training for image captioning"

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.CV(Computer Vision and Pattern Recognition)