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: "sshmt semi supervised hierarchical merge tree for electron microscopy image segmentation"
  Abstract: Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determine region merging. Such functions are usually learned with supervised algorithms that demand considerable ground truth data, which are costly to collect. We propose a semi-supervised approach that reduces this demand. Based on a merge tree structure, we develop a differentiable unsupervised loss term that enforces consistent predictions from the learned function. We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning. The experimental results on three EM data sets demonstrate that by using a subset of only \(3\,\%\) to \(7\,\%\) of the entire ground truth data, our approach consistently performs close to the state-of-the-art supervised method with the full labeled data set, and significantly outperforms the supervised method with the same labeled subset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "mask r cnn"
    - "training recurrent neural networks by diffusion"
    - "fully automatic synapse prediction and validation on a large data set"
    - "flood filling networks"
    - "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)