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: "image captioning and visual question answering based on attributes and their related external knowledge"
  Abstract: Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we first propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We further show that the same mechanism can be used to incorporate external knowledge, which is critically important for answering high level visual questions. Specifically, we design a visual question answering model that combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain a complete answer. Our final model achieves the best reported results on both image captioning and visual question answering on several benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "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-2 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering"):
  with members:
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-3 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "creativity generating diverse questions using variational autoencoders"):
  with members:
    - "improved techniques for training gans"
    - "multimodal residual learning for visual qa"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"
    - "revisiting visual question answering baselines"
    - "dynamic memory networks for visual and textual question answering"
    - "measuring machine intelligence through visual question answering"

Connected through hyperedge HE-5 (induced by source paper "attributes as semantic units between natural language and visual recognition"):
  with members:
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "segmentation from natural language expressions"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "yfcc100m the new data in multimedia research"
    - "generation and comprehension of unambiguous object descriptions"

Connected through hyperedge HE-6 (induced by source paper "visual question answering a survey of methods and datasets"):
  with members:
    - "training recurrent answering units with joint loss minimization for vqa"
    - "language to logical form with neural attention"
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "a diagram is worth a dozen images"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-7 (induced by source paper "clevr a diagnostic dataset for compositional language and elementary visual reasoning"):
  with members:
    - "analyzing the behavior of visual question answering models"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"
    - "revisiting visual question answering baselines"
    - "dynamic memory networks for visual and textual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"

Connected through hyperedge HE-8 (induced by source paper "revisiting visual question answering baselines"):
  with members:
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "hierarchical question image co attention for visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"
    - "deep compositional question answering with neural module networks"
    - "vqa visual question answering"

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)