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: "automatic image segmentation by dynamic region merging"
  Abstract: This paper addresses the automatic image segmentation problem in a region merging style. With an initially oversegmented image, in which many regions (or superpixels) with homogeneous color are detected, an image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region-merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test and the minimal cost criterion. Starting from an oversegmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates the image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region-merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region-merging algorithm.

Connected through hyperedge HE-1 (induced by source paper "adaptive morphological reconstruction for seeded image segmentation"):
  with members:
    - "improved image segmentation via cost minimization of multiple hypotheses"
    - "image segmentation using sparse subset selection"
    - "revisiting graph construction for fast image segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "conditional random fields as recurrent neural networks"
    - "superpixel convolutional networks using bilateral inceptions"
    - "oriented edge forests for boundary detection"

Connected through hyperedge HE-2 (induced by source paper "topomap topological mapping and navigation based on visual slam maps"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-3 (induced by source paper "local variation as a statistical hypothesis test"):
  with members:
    - "predicting important objects for egocentric video summarization"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-4 (induced by source paper "joint color spatial directional clustering and region merging jcsd rm for unsupervised rgb d image segmentation"):
  with members:
    - "statistical exponential families a digest with flash cards"

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)