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: "video question answering via attribute augmented attention network learning"
  Abstract: Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "multi step joint modality attention network for scene aware dialogue system"):
  with members:
    - "dual attention networks for visual reference resolution in visual dialog"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "bilinear attention networks"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "tvqa localized compositional video question answering"
    - "recursive visual attention in visual dialog"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-2 (induced by source paper "learning question guided video representation for multi turn video question answering"):
  with members:
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "reactive multi stage feature fusion for multimodal dialogue modeling"
    - "audio visual scene aware dialog"
    - "motion appearance co memory networks for video question answering"
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"

Connected through hyperedge HE-3 (induced by source paper "zero shot visual recognition using semantics preserving adversarial embedding networks"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "natural and effective obfuscation by head inpainting"
    - "unified deep supervised domain adaptation and generalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "semantically consistent regularization for zero shot recognition"

Connected through hyperedge HE-4 (induced by source paper "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "person search with natural language description"
    - "hadamard product for low rank bilinear pooling"
    - "a focused dynamic attention model for visual question answering"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"

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)