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 "texture aware superpixel segmentation"):
  Abstract: Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.

Includes members:
  Member "evaluation framework of superpixel methods with a global regularity measure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "superpatchmatch an algorithm for robust correspondences using superpixel patches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "robust superpixels using color and contour features along linear path":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "texture superpixel clustering from patch based nearest neighbor matching")
  Member "superpixel convolutional networks using bilateral inceptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gated scnn gated shape cnns for semantic segmentation")
  Member "gasp geometric association with surface patches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")

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)