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: "image segmentation using hierarchical merge tree"
  Abstract: This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with oversegmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other recent methods on six public data sets demonstrate that our approach achieves the state-of-the-art region accuracy and is competitive in image segmentation without semantic priors.

Connected through hyperedge HE-1 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "instance segmentation by deep coloring"
    - "affinity derivation and graph merge for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "cell detection with star convex polygons"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-2 (induced by source paper "topologically guided color image enhancement"):
  with members:
    - "propagate and pair a single pass approach to critical point pairing in reeb graphs"
    - "the shape of an image a study of mapper on images"
    - "exposure a white box photo post processing framework"

Connected through hyperedge HE-3 (induced by source paper "image segmentation using sparse subset selection"):
  with members:
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "lifting gis maps into strong geometric context for scene understanding"
    - "learning deconvolution network for semantic segmentation"
    - "pushing the boundaries of boundary detection using deep learning"
    - "simultaneous detection and segmentation"

Connected through hyperedge HE-4 (induced by source paper "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"):
  with members:
    - "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "natural scene image segmentation based on multi layer feature extraction"):
  with members:
    - "generalized boundaries from multiple image interpretations"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-6 (induced by source paper "sshmt semi supervised hierarchical merge tree for electron microscopy image segmentation"):
  with members:
    - "machine learning of hierarchical clustering to segment 2d and 3d images"

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)