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: "deep reinforcement learning based image captioning with embedding reward"
  Abstract: Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a "policy network" and a "value network" to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "gaussian smoothen semantic features gssf exploring the linguistic aspects of visual captioning in indian languages bengali using mscoco framework"):
  with members:
    - "neural baby talk"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "pragmatically informative image captioning with character level reference"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"
    - "improved image captioning via policy gradient optimization of spider"
    - "actor critic sequence training for image captioning"
    - "mat a multimodal attentive translator for image captioning"

Connected through hyperedge HE-3 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling"):
  with members:
    - "hierarchical photo scene encoder for album storytelling"
    - "linknet relational embedding for scene graph"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "attention is all you need"
    - "learning by asking questions"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"

Connected through hyperedge HE-4 (induced by source paper "image to language understanding captioning approach"):
  with members:
    - "contextual lstm clstm models for large scale nlp tasks"
    - "using sentence level lstm language models for script inference"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "rethinking the inception architecture for computer vision"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-5 (induced by source paper "vision and language from visual perception to content creation"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "hierarchy parsing for image captioning"
    - "unsupervised person image generation with semantic parsing transformation"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "generating multiple objects at spatially distinct locations"
    - "joint discriminative and generative learning for person re identification"
    - "relationship aware spatial perception fusion for realistic scene layout generation"
    - "photo realistic facial details synthesis from single image"

Connected through hyperedge HE-6 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "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"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-7 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "which encoding is the best for text classification in chinese english japanese and korean"

Connected through hyperedge HE-8 (induced by source paper "controllable video captioning with pos sequence guidance based on gated fusion network"):
  with members:
    - "reconstruct and represent video contents for captioning via reinforcement learning"
    - "hierarchical photo scene encoder for album storytelling"
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "unsupervised image captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network 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)