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 "superpixel based class semantic texton occurrences for natural roadside vegetation segmentation"):
  Abstract: Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition monitoring. In this paper, we present a novel approach that generates class-semantic color---texture textons and aggregates superpixel-based texton occurrences for vegetation segmentation in natural roadside images. Pixel-level class-semantic textons are learnt by generating two individual sets of bag-of-word visual dictionaries from color and filter bank texture features separately for each object class using manually cropped training data. For a testing image, it is first oversegmented into a set of homogeneous superpixels. The color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric, resulting in a color and a texture texton occurrence matrix. The color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy. Evaluations on two datasets such as video data collected by the Department of Transport and Main Roads, Queensland, Australia, and a public roadside grass dataset show high accuracy of the proposed approach. We also demonstrate the effectiveness of the approach for vegetation segmentation in real-world scenarios.

Includes members:
  Member "hybrid cnn and dictionary based models for scene recognition and domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic aware scene recognition")
  Member "spatially constrained location prior for scene parsing":
    also connected through other hyperedges:
      (none)
  Member "good practice in cnn feature transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to see across domains and modalities")
  Member "a survey of sparse representation algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "deep hierarchical parsing for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "red net a recursive encoder decoder network for edge detection")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "recurrent convolutional neural networks for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sos selective objective switch for rapid immunofluorescence whole slide image classification")

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)