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: "a context aware delayed agglomeration framework for electron microscopy segmentation"
  Abstract: Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a “delayed” scheme for agglomerative clustering that postpones some of the merge decisions, pertaining to newly formed bodies, in order to generate a more confident boundary prediction. We report significant improvements attained by the proposed approach in segmentation accuracy over existing standard methods on 2D and 3D datasets.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "morphological error detection in 3d segmentations"):
  with members:
    - "toward streaming synapse detection with compositional convnets"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "deep tensor convolution on multicores"
    - "flood filling networks"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "toward streaming synapse detection with compositional convnets"):
  with members:
    - "deep tensor convolution on multicores"
    - "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"
    - "very deep convolutional networks for large scale image recognition"
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "anisotropic em segmentation by 3d affinity learning and agglomeration"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"
    - "identifying synapses using deep and wide multiscale recursive networks"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"

Connected through hyperedge HE-5 (induced by source paper "santiago spine association for neuron topology improvement and graph optimization"):
  with members:
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"

Connected through hyperedge HE-6 (induced by source paper "what properties are desirable from an electron microscopy segmentation algorithm"):
  with members:
    - "machine learning of hierarchical clustering to segment 2d and 3d images"
    - "fast image scanning with deep max pooling convolutional neural networks"
    - "deep and wide multiscale recursive networks for robust image labeling"

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)