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: "stack captioning coarse to fine learning for image captioning"
  Abstract: The existing image captioning approaches typically train a one-stage sentence decoder, which is difficult to generate rich fine-grained descriptions. On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem. In this paper, we propose a coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions. Our proposed learning approach addresses the difficulty of vanishing gradients during training by providing a learning objective function that enforces intermediate supervisions. Particularly, we optimize our model with a reinforcement learning approach which utilizes the output of each intermediate decoder's test-time inference algorithm as well as the output of its preceding decoder to normalize the rewards, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem. We extensively evaluate the proposed approach on MSCOCO and show that our approach can achieve the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "dynamic graph attention for referring expression comprehension"
    - "visual semantic reasoning for image text matching"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "visual reasoning with multi hop feature modulation"
    - "identity aware textual visual matching with latent co attention"

Connected through hyperedge HE-2 (induced by source paper "learning to collocate neural modules for image captioning"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "context aware visual policy network for fine grained image captioning"
    - "context and attribute grounded dense captioning"
    - "finding syntax in human encephalography with beam search"
    - "bilinear attention networks"
    - "neural baby talk"
    - "learning to assemble neural module tree networks for visual grounding"

Connected through hyperedge HE-3 (induced by source paper "pr product a substitute for inner product in neural networks"):
  with members:
    - "neural baby talk"
    - "context aware visual policy network for sequence level image captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "recurrent fusion network for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-4 (induced by source paper "unpaired image captioning via scene graph alignments"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "engaging image captioning via personality"

Connected through hyperedge HE-5 (induced by source paper "watch it twice video captioning with a refocused video encoder"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "less is more picking informative frames for video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reconstruction network for video captioning"
    - "unpaired image captioning by language pivoting"
    - "end to end video captioning with multitask reinforcement learning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-6 (induced by source paper "context and attribute grounded dense captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "scene graph generation with external knowledge and image reconstruction"):
  with members:
    - "semantic correlation promoted shape variant context for segmentation"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "graph r cnn for scene graph generation"
    - "scene graph parsing as dependency parsing"
    - "auto encoding scene graphs for image captioning"
    - "unpaired image captioning by language pivoting"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-8 (induced by source paper "streamlined dense video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"

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)