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: "out of the box reasoning with graph convolution nets for factual visual question answering"
  Abstract: Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains an algorithmic challenge. To advance research in this direction a novel `fact-based' visual question answering (FVQA) task has been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation. Given a question-image pair, deep network techniques have been employed to successively reduce the large set of facts until one of the two entities of the final remaining fact is predicted as the answer. We observe that a successive process which considers one fact at a time to form a local decision is sub-optimal. Instead, we develop an entity graph and use a graph convolutional network to `reason' about the correct answer by jointly considering all entities. We show on the challenging FVQA dataset that this leads to an improvement in accuracy of around 7% compared to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning graph embedding with adversarial training methods"
    - "attributed graph clustering a deep attentional embedding approach"
    - "label efficient semi supervised learning via graph filtering"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "review of visual saliency detection with comprehensive information"
    - "iterative visual reasoning beyond convolutions"

Connected through hyperedge HE-2 (induced by source paper "object relational graph with teacher recommended learning for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "controllable video captioning with pos sequence guidance based on gated fusion network"
    - "memory attended recurrent network for video captioning"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "relation aware graph attention network for visual question answering"
    - "less is more picking informative frames for video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "learning global and local consistent representations for unsupervised image retrieval via deep graph diffusion networks"):
  with members:
    - "estimating node importance in knowledge graphs using graph neural networks"
    - "neural graph collaborative filtering"
    - "iterative visual reasoning beyond convolutions"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "end to end learning of deep visual representations for image retrieval"
    - "gated graph sequence neural networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "reasoning visual dialogs with structural and partial observations"):
  with members:
    - "flipdial a generative model for two way visual dialogue"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "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"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-5 (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"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "structured attention networks"

Connected through hyperedge HE-6 (induced by source paper "hypergraph convolution and hypergraph attention"):
  with members:
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-7 (induced by source paper "ok vqa a visual question answering benchmark requiring external knowledge"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an implementation of faster rcnn with study for region sampling"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"

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)