human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a joint convolutional neural networks and context transfer for street scenes labeling"):
  Abstract: Street scene understanding is an essential task for autonomous driving. One important step toward this direction is scene labeling, which annotates each pixel in the images with a correct class label. Although many approaches have been developed, there are still some weak points. First, many methods are based on the hand-crafted features whose image representation ability is limited. Second, they cannot label foreground objects accurately due to the data set bias. Third, in the refinement stage, the traditional Markov random filed inference is prone to over smoothness. For improving the above problems, this paper proposes a joint method of priori convolutional neural networks at superpixel level (called as “priori s-CNNs”) and soft restricted context transfer. Our contributions are threefold: 1)  a priori  s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; 2) a hierarchical data augmentation method is presented to alleviate data set bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; and 3) a soft restricted MRF energy function is defined to improve the priori s-CNNs model’s labeling performance and reduce the over smoothness at the same time. The proposed approach is verified on CamVid data set (11 classes) and SIFT Flow Street data set (16 classes) and achieves a competitive performance.

Includes members:
  Member "a general framework for edited video and raw video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning for task driven video summarization")
  Member "anomaly detection in traffic scenes via spatial aware motion reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "embedding structured contour and location prior in siamesed fully convolutional networks for road detection")
  Member "automatic subspace learning via principal coefficients embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust subspace clustering with compressed data")
  Member "deep image scaling up image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic optimization of plain convolutional neural networks with simple methods")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "simultaneous detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "some improvements on deep convolutional neural network based image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a simple framework for contrastive learning of visual representations")

Question: Please tell me which class the source paper 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)