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: "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
  Abstract: Visual Question Answering (VQA) has attracted much attention since it offers insight into the relationships between the multi-modal analysis of images and natural language. Most of the current algorithms are incapable of answering open-domain questions that require to perform reasoning beyond the image contents. To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks. Specifically, the questions along with the corresponding images trigger a process to retrieve the relevant information in external knowledge bases, which are embedded into a continuous vector space by preserving the entity-relation structures. Afterwards, we employ dynamic memory networks to attend to the large body of facts in the knowledge graph and images, and then perform reasoning over these facts to generate corresponding answers. Extensive experiments demonstrate that our model not only achieves the state-of-the-art performance in the visual question answering task, but can also answer open-domain questions effectively by leveraging the external knowledge.

Connected through hyperedge HE-1 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-2 (induced by source paper "relation aware graph attention network for visual question answering"):
  with members:
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "differential attention for visual question answering"
    - "learning visual knowledge memory networks for visual question answering"

Connected through hyperedge HE-3 (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"
    - "out of the box reasoning with graph convolution nets 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"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-4 (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-5 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "vqa with no questions answers training"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "r vqa learning visual relation facts with semantic attention for visual question answering"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "a context aware attention network for interactive question answering"
    - "hadamard product for low rank bilinear pooling"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "visual question answering as reading comprehension"):
  with members:
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reading wikipedia to answer open domain questions"
    - "squad 100 000 questions for machine comprehension of text"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "revisiting visual question answering baselines"
    - "the vqa machine learning how to use existing vision algorithms to answer new questions"

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)