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: "factual or emotional stylized image captioning with adaptive learning and attention"
  Abstract: Generating stylized captions for an image is an emerging topic in image captioning. Given an image as input, it requires the system to generate a caption that has a specific style (e.g., humorous, romantic, positive, and negative) while describing the image content semantically accurately. In this paper, we propose a novel stylized image captioning model that effectively takes both requirements into consideration. To this end, we first devise a new variant of LSTM, named style-factual LSTM, as the building block of our model. It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context. In addition, when we train the model to capture stylized elements, we propose an adaptive learning approach based on a reference factual model, it provides factual knowledge to the model as the model learns from stylized caption labels, and can adaptively compute how much information to supply at each time step. We evaluate our model on two stylized image captioning datasets, which contain humorous/romantic captions and positive/negative captions, respectively. Experiments shows that our proposed model outperforms the state-of-the-art approaches, without using extra ground truth supervision.

Connected through hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "multimodal image captioning for marketing analysis"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"

Connected through hyperedge HE-2 (induced by source paper "adaptive offline quintuplet loss for image text matching"):
  with members:
    - "expressing objects just like words recurrent visual embedding for image text matching"
    - "visual semantic reasoning for image text matching"
    - "saliency guided attention network for image sentence matching"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "hal improved text image matching by mitigating visual semantic hubs"
    - "identity aware textual visual matching with latent co attention"
    - "dual path convolutional image text embedding with instance loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "reasoning visual dialogs with structural and partial observations"):
  with members:
    - "flipdial a generative model for two way visual dialogue"
    - "learning visual question answering by bootstrapping hard attention"
    - "discriminability objective for training descriptive captions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning human object interactions by graph parsing neural networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-4 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "face cap image captioning using facial expression analysis"
    - "automatic recognition of student engagement using deep learning and facial expression"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "a comprehensive survey of deep learning for image captioning"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "context and attribute grounded dense captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "discriminability objective for training descriptive captions"
    - "modeling visual context is key to augmenting object detection datasets"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-6 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "boosted attention leveraging human attention for image captioning"
    - "object part attention model for fine grained image classification"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "semantic aware attention based deep object co segmentation"
    - "cross view image synthesis using conditional gans"
    - "harmonious attention network for person re identification"
    - "global pose estimation with an attention based recurrent network"

Connected through hyperedge HE-7 (induced by source paper "construct dynamic graphs for hand gesture recognition via spatial temporal attention"):
  with members:
    - "semantic graph convolutional networks for 3d human pose regression"
    - "quantized densely connected u nets for efficient landmark localization"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cr gan learning complete representations for multi view generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-8 (induced by source paper "towards generating stylized image captions via adversarial training"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "recurrent topic transition gan for visual paragraph generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "image to image translation with conditional adversarial networks"

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)