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: "morphological error detection in 3d segmentations"
  Abstract: Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans, by contrast, can easily detect such errors by acquiring intuition for the correct morphology of objects. Biological neurons have complicated and variable shapes, which are challenging to learn, and merge errors take a multitude of different forms. We present an algorithm, MergeNet, that shows 3D ConvNets can, in fact, detect merge errors from high-level neuronal morphology. MergeNet follows unsupervised training and operates across datasets. We demonstrate the performance of MergeNet both on a variety of connectomics data and on a dataset created from merged MNIST images.

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 "leveraging domain knowledge to improve em image segmentation with lifted multicuts"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "cross classification clustering an efficient multi object tracking technique for 3 d instance segmentation in connectomics"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "u net convolutional networks for biomedical image segmentation"
    - "analysis and optimization of graph decompositions by lifted multicuts"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "scikit image image processing in python"

Connected through hyperedge HE-3 (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"
    - "an error detection and correction framework for connectomics"
    - "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"

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)