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 "dynamic spectral residual superpixels"):
  Abstract: We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects. Our novel approach builds upon the widely used Simple Linear Iterative Clustering (SLIC), and incorporate a measure of objects' structure based on the spectral residual of an image. Based on this combination, we propose a modified initialisation scheme and search metric, which helps keeps fine-details. This combination leads to better adherence to object boundaries, while preventing unnecessary segmentation of large, uniform areas, while remaining computationally tractable in comparison to other methods. We demonstrate through numerical and visual experiments that our approach outperforms the state-of-the-art techniques.

Includes members:
  Member "superpixel contracted graph based learning for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")
  Member "superpixel based color transfer":
    also connected through other hyperedges:
      (none)
  Member "selflow self supervised learning of optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")
  Member "superpixels an evaluation of the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "peekaboo where are the objects structure adjusting superpixels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "superpixel contracted graph based learning for hyperspectral image classification")
  Member "accurate light field depth estimation with superpixel regularization over partially occluded regions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "style transfer for light field photography")
  Member "deep contrast learning for salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "deep convolutional neural fields for depth estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")

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)