human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "leveraging domain knowledge to improve em image segmentation with lifted multicuts"):
  Abstract: The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural networks, their accuracy still lags behind human annotations and requires additional manual proof-reading. A major hindrance to further improvements is the limited field of view of the segmentation networks preventing them from exploiting the expected cell morphology or other prior biological knowledge which humans use to inform their segmentation decisions. In this contribution, we show how such domain-specific information can be leveraged by expressing it as long-range interactions in a graph partitioning problem known as the lifted multicut problem. Using this formulation, we demonstrate significant improvement in segmentation accuracy for three challenging EM segmentation problems from neuroscience and cell biology.

Includes members:
  Member "the mutex watershed and its objective efficient parameter free image partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation")
  Member "cross classification clustering an efficient multi object tracking technique for 3 d instance segmentation in connectomics":
    also connected through other hyperedges:
      (none)
  Member "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images")
  Member "superhuman accuracy on the snemi3d connectomics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non local u net for biomedical image segmentation")
  Member "morphological error detection in 3d segmentations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "analysis and optimization of graph decompositions by lifted multicuts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "efficient decomposition of image and mesh graphs by lifted multicuts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep affinity net instance segmentation via affinity")

Question: Please tell me which class the source paper 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)