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: "hierarchical co attention for visual question answering"
  Abstract: A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question and consequently the image via the co-attention mechanism in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN) model. Our final model outperforms all reported methods, improving the state-of-the-art on the VQA dataset from 60.4% to 62.1%, and from 61.6% to 65.4% on the COCO-QA dataset.

Connected through hyperedge HE-1 (induced by source paper "less is more picking informative frames for video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "reconstruction network for video captioning"
    - "supervising neural attention models for video captioning by human gaze data"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "image captioning with semantic attention"
    - "frame and segment level features and candidate pool evaluation for video caption generation"

Connected through hyperedge HE-2 (induced by source paper "learning to reason end to end module networks for visual question answering"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "segmentation from natural language expressions"
    - "learning to compose neural networks for question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "revisiting visual question answering baselines"
    - "dynamic memory networks for visual and textual question answering"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "ask your neurons a deep learning approach to visual question answering"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "segmentation from natural language expressions"
    - "tutorial on answering questions about images with deep learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multimodal compact bilinear pooling for visual question answering and visual grounding"):
  with members:
    - "a focused dynamic attention model for visual question answering"
    - "segmentation from natural language expressions"
    - "ask your neurons a deep learning approach to visual question answering"
    - "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"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic memory networks for visual and textual 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)