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: "dense image representation with spatial pyramid vlad coding of cnn for locally robust captioning"
  Abstract: The workflow of extracting features from images using convolutional neural networks (CNN) and generating captions with recurrent neural networks (RNN) has become a de-facto standard for image captioning task. However, since CNN features are originally designed for classification task, it is mostly concerned with the main conspicuous element of the image, and often fails to correctly convey information on local, secondary elements. We propose to incorporate coding with vector of locally aggregated descriptors (VLAD) on spatial pyramid for CNN features of sub-regions in order to generate image representations that better reflect the local information of the images. Our results show that our method of compact VLAD coding can match CNN features with as little as 3% of dimensionality and, when combined with spatial pyramid, it results in image captions that more accurately take local elements into account.

Connected through hyperedge HE-1 (induced by source paper "multiple vlad encoding of cnns for image classification"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "fast r cnn"
    - "design of kernels in convolutional neural networks for image classification"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-2 (induced by source paper "traffic scene recognition based on deep cnn and vlad spatial pyramids"):
  with members:
    - "matconvnet convolutional neural networks for matlab"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-3 (induced by source paper "dualnet domain invariant network for visual question answering"):
  with members:
    - "a focused dynamic attention model for visual question answering"
    - "ask your neurons a deep learning approach to visual question answering"
    - "multimodal residual learning for visual qa"
    - "learning to compose neural networks for question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "vqa visual question answering"
    - "image question answering using convolutional neural network with dynamic parameter prediction"

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)