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: "an error detection and correction framework for connectomics"
  Abstract: We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object. We train multiscale 3D convolutional networks to perform both tasks. We find that the error-detecting net can achieve high accuracy. The accuracy of the error-correcting net is enhanced if its input object mask is "advice" (union of erroneous objects) from the error-detecting net.

Connected through hyperedge HE-1 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy"):
  with members:
    - "synaptic partner assignment using attentional voxel association networks"
    - "siamese encoding and alignment by multiscale learning with self supervision"
    - "chunkflow distributed hybrid cloud processing of large 3d images by convolutional nets"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "synaptic partner prediction from point annotations in insect brains"
    - "joint deformable registration of large em image volumes a matrix solver approach"

Connected through hyperedge HE-2 (induced by source paper "synaptic partner assignment using attentional voxel association networks"):
  with members:
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "synaptic partner prediction from point annotations in insect brains"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "toward streaming synapse detection with compositional convnets"

Connected through hyperedge HE-3 (induced by source paper "learning metric graphs for neuron segmentation in electron microscopy images"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "a tutorial on principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "leveraging domain knowledge to improve microscopy image segmentation with lifted multicuts"):
  with members:
    - "scikit image image processing in python"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"

Connected through hyperedge HE-5 (induced by source paper "cross classification clustering an efficient multi object tracking technique for 3 d instance segmentation in connectomics"):
  with members:
    - "instance segmentation by deep coloring"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "morphological error detection in 3d segmentations"
    - "detect and track efficient pose estimation in videos"
    - "large margin object tracking with circulant feature maps"
    - "object detection from video tubelets with convolutional neural networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "guided proofreading of automatic segmentations for connectomics"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "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)