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: "peekaboo where are the objects structure adjusting superpixels"
  Abstract: This paper addresses the search for a fast and meaningful image segmentation in the context of $k$-means clustering. The proposed method builds on a widely-used local version of Lloyd's algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in the image, and thus provides for more meaningful superpixels in the same linear runtime as standard SLIC. The proposed method is evaluated against state-of-the-art techniques and improved boundary adherence and undersegmentation error are observed, whilst still remaining among the fastest algorithms which are tested.

Connected through hyperedge HE-1 (induced by source paper "dynamic spectral residual superpixels"):
  with members:
    - "superpixel contracted graph based learning for hyperspectral image classification"
    - "superpixel based color transfer"
    - "selflow self supervised learning of optical flow"
    - "superpixels an evaluation of the state of the art"
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "deep contrast learning for salient object detection"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-2 (induced by source paper "superpixel contracted graph based learning for hyperspectral image classification"):
  with members:
    - "semi supervised learning with graphs covariance based superpixels for hyperspectral image classification"
    - "superpixels an evaluation of the state of the art"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning with graphs covariance based superpixels for hyperspectral image classification"):
  with members:
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"

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)