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: "utilizing large scale vision and text datasets for image segmentation from referring expressions"
  Abstract: Image segmentation from referring expressions is a joint vision and language modeling task, where the input is an image and a textual expression describing a particular region in the image; and the goal is to localize and segment the specific image region based on the given expression. One major difficulty to train such language-based image segmentation systems is the lack of datasets with joint vision and text annotations. Although existing vision datasets such as MS COCO provide image captions, there are few datasets with region-level textual annotations for images, and these are often smaller in scale. In this paper, we explore how existing large scale vision-only and text-only datasets can be utilized to train models for image segmentation from referring expressions. We propose a method to address this problem, and show in experiments that our method can help this joint vision and language modeling task with vision-only and text-only data and outperforms previous results.

Connected through hyperedge HE-1 (induced by source paper "dynamic multimodal instance segmentation guided by natural language queries"):
  with members:
    - "dual path networks"
    - "recurrent multimodal interaction for referring image segmentation"
    - "semantic compositional networks for visual captioning"
    - "review networks for caption generation"
    - "segmentation from natural language expressions"
    - "densely connected convolutional networks"
    - "graph structured representations for visual question answering"
    - "modeling context in referring expressions"

Connected through hyperedge HE-2 (induced by source paper "recurrent multimodal interaction for referring image segmentation"):
  with members:
    - "mat a multimodal attentive translator for image captioning"
    - "unsupervised learning for physical interaction through video prediction"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "review networks for caption generation"
    - "attention correctness in neural image captioning"
    - "segmentation from natural language expressions"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-3 (induced by source paper "segmentation from natural language expressions"):
  with members:
    - "grounding of textual phrases in images by reconstruction"
    - "learning to compose neural networks for question answering"
    - "generation and comprehension of unambiguous object descriptions"
    - "conditional random fields as recurrent neural networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "multi scale context aggregation by dilated convolutions"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"

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)