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: "extracting visual knowledge from the internet making sense of image data"
  Abstract: Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data. Extensive research has been devoted to the first two, but much less attention has been paid to the third. Due to the high cost of manual labeling, the size of recent efforts such as ImageNet is still relatively small in respect to daily applications. In this work, we mainly focus on how to automatically generate identifying image data for a given visual concept on a vast scale. With the generated image data, we can train a robust recognition model for the given concept. We evaluate the proposed webly supervised approach on the benchmark Pascal VOC 2007 dataset and the results demonstrates the superiority of our proposed approach in image data collection.

Connected through hyperedge HE-1 (induced by source paper "road segmentation with image lidar data fusion"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "exploiting web images for dataset construction a domain robust approach"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep representation learning for road detection through siamese network"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "exploiting web images for dataset construction a domain robust approach"
    - "mask r cnn"
    - "multinet real time joint semantic reasoning for autonomous driving"

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)