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: "kernelized deep convolutional neural network for describing complex images"
  Abstract: With the impressive capability to capture visual content, deep convolutional neural networks (CNN) have demon- strated promising performance in various vision-based ap- plications, such as classification, recognition, and objec- t detection. However, due to the intrinsic structure design of CNN, for images with complex content, it achieves lim- ited capability on invariance to translation, rotation, and re-sizing changes, which is strongly emphasized in the s- cenario of content-based image retrieval. In this paper, to address this problem, we proposed a new kernelized deep convolutional neural network. We first discuss our motiva- tion by an experimental study to demonstrate the sensitivi- ty of the global CNN feature to the basic geometric trans- formations. Then, we propose to represent visual content with approximate invariance to the above geometric trans- formations from a kernelized perspective. We extract CNN features on the detected object-like patches and aggregate these patch-level CNN features to form a vectorial repre- sentation with the Fisher vector model. The effectiveness of our proposed algorithm is demonstrated on image search application with three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "open ended visual question answering"):
  with members:
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"
    - "dynamic memory networks for visual and textual question answering"
    - "learning models for actions and person object interactions with transfer to 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-2 (induced by source paper "vibiknet visual bidirectional kernelized network for visual question answering"):
  with members:
    - "dual attention networks for multimodal reasoning and matching"
    - "video description using bidirectional recurrent neural networks"
    - "multimodal residual learning for visual qa"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "microsoft coco captions data collection and evaluation server"
    - "adam a method for stochastic optimization"

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)