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: "superpixels an evaluation of the state of the art"
  Abstract: Abstract   Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003 (Ren and Malik, 2003). By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at  http://www.davidstutz.de/projects/superpixel-benchmark/ .

Connected through hyperedge HE-1 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors"):
  with members:
    - "texture superpixel clustering from patch based nearest neighbor matching"
    - "evaluation framework of superpixel methods with a global regularity measure"
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "universal adversarial perturbations"
    - "u net convolutional networks for biomedical image segmentation"
    - "gasp geometric association with surface patches"

Connected through hyperedge HE-2 (induced by source paper "texture superpixel clustering from patch based nearest neighbor matching"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "robust superpixels using color and contour features along linear path"
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"
    - "feedforward semantic segmentation with zoom out features"

Connected through hyperedge HE-3 (induced by source paper "unsupervised community detection with a potts model hamiltonian an efficient algorithmic solution and applications in digital pathology"):
  with members:
    - "a wardpβ"

Connected through hyperedge HE-4 (induced by source paper "point cloud oversegmentation with graph structured deep metric learning"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "group normalization"
    - "superpixel sampling networks"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "graph based over segmentation methods for 3d point clouds"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-5 (induced by source paper "freelabel a publicly available annotation tool based on freehand traces"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "semantic segmentation refinement by monte carlo region growing of high confidence detections"
    - "iteratively trained interactive segmentation"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "apple flower detection using deep convolutional networks"
    - "looking at outfit to parse clothing"
    - "mask r cnn"

Connected through hyperedge HE-6 (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"
    - "peekaboo where are the objects structure adjusting superpixels"
    - "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-7 (induced by source paper "3d quantum cuts for automatic segmentation of porous media in tomography images"):
  with members:
    - "salient object detection a survey"
    - "probabilistic saliency estimation"
    - "salient object detection a discriminative regional feature integration approach"
    - "what is a salient object a dataset and a baseline model for salient object detection"
    - "the secrets of salient object segmentation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-8 (induced by source paper "evaluation framework of superpixel methods with a global regularity measure"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "robust shape regularity criteria for superpixel evaluation"
    - "robust superpixels using color and contour features along linear path"
    - "superpixel convolutional networks using bilateral inceptions"
    - "gasp geometric association with surface patches"
    - "deep convolutional neural fields for depth estimation from a single image"

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)