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: "learned watershed end to end learning of seeded segmentation"
  Abstract: Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is con- volutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method gives the best known seeded segmentation results on the CREMI segmentation challenge.

Connected through hyperedge HE-1 (induced by source paper "patchperpix for instance segmentation"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "tensormask a foundation for dense object segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "cell detection with star convex polygons"
    - "semi convolutional operators for instance segmentation"
    - "instance sensitive fully convolutional networks"
    - "deep watershed transform for instance segmentation"

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 "the mutex watershed and its objective efficient parameter free image partitioning"):
  with members:
    - "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"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-4 (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"
    - "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"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"

Connected through hyperedge HE-5 (induced by source paper "end to end learned random walker for seeded image segmentation"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "optnet differentiable optimization as a layer in neural networks"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "convolutional random walk networks for semantic image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "probabilistic watershed sampling all spanning forests for seeded segmentation and semi supervised learning"):
  with members:
    - "end to end learned random walker for seeded image segmentation"
    - "stochastic distance transform"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-7 (induced by source paper "cnn based preprocessing to optimize watershed based cell segmentation in 3d confocal microscopy images"):
  with members:
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "segment3d a web based application for collaborative segmentation of 3d images used in the shoot apical meristem"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep watershed transform for instance segmentation"
    - "u net convolutional networks for biomedical 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)