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 "cross classification clustering an efficient multi object tracking technique for 3 d instance segmentation in connectomics"):
  Abstract: Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching. Here we introduce cross-classification clustering (3C), a technique that simultaneously tracks complex, interrelated objects in an image stack. The key idea in cross-classification is to efficiently turn a clustering problem into a classification problem by running a logarithmic number of independent classifications per image, letting the cross-labeling of these classifications uniquely classify each pixel to the object labels. We apply the 3C mechanism to achieve state-of-the-art accuracy in connectomics -- the nanoscale mapping of neural tissue from electron microscopy volumes. Our reconstruction system increases scalability by an order of magnitude over existing single-object tracking methods (such as flood-filling networks). This scalability is important for the deployment of connectomics pipelines, since currently the best performing techniques require computing infrastructures that are beyond the reach of most laboratories. Our algorithm may offer benefits in other domains that require pixel-accurate tracking of multiple objects, such as segmentation of videos and medical imagery.

Includes members:
  Member "instance segmentation by deep coloring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "superhuman accuracy on the snemi3d connectomics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non local u net for biomedical image segmentation")
  Member "anisotropic em segmentation by 3d affinity learning and agglomeration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "morphological error detection in 3d segmentations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "an error detection and correction framework for connectomics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "detect and track efficient pose estimation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonparametric structure regularization machine for 2d hand pose estimation")
  Member "large margin object tracking with circulant feature maps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "object detection from video tubelets with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking temporal object detection from robotic perspectives")

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)