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: "machine learning of hierarchical clustering to segment 2d and 3d images"
  Abstract: We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.

Connected through hyperedge HE-1 (induced by source paper "efficient 2d neuron boundary segmentation with local topological constraints"):
  with members:
    - "modeling brain circuitry over a wide range of scales"
    - "ted a tolerant edit distance for segmentation evaluation"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "instance segmentation by deep coloring"
    - "affinity derivation and graph merge for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "cell detection with star convex polygons"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-4 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction"):
  with members:
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "on the convergence of adam and beyond"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"

Connected through hyperedge HE-5 (induced by source paper "the mutex watershed and its objective efficient parameter free image partitioning"):
  with members:
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "learning steerable filters for rotation equivariant cnns"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-6 (induced by source paper "scaling distributed training of flood filling networks on hpc infrastructure for brain mapping"):
  with members:
    - "an empirical model of large batch training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-7 (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"
    - "morphological error detection in 3d segmentations"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "wpu net boundary learning by using weighted propagation in convolution network"):
  with members:
    - "a novel focal tversky loss function with improved attention u net for lesion segmentation"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "attention u net learning where to look for the pancreas"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)