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 "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy"):
  Abstract: Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes.

Includes members:
  Member "synaptic partner assignment using attentional voxel association networks":
    also connected through other hyperedges:
      (none)
  Member "siamese encoding and alignment by multiscale learning with self supervision":
    also connected through other hyperedges:
      (none)
  Member "chunkflow distributed hybrid cloud processing of large 3d images by convolutional nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyper distributed cloud processing for large scale deep learning tasks")
  Member "learning metric graphs for neuron segmentation in electron microscopy images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction")
  Member "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "synaptic partner assignment using attentional voxel association networks")
  Member "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images")
  Member "synaptic partner prediction from point annotations in insect brains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "synaptic partner assignment using attentional voxel association networks")
  Member "joint deformable registration of large em image volumes a matrix solver approach":
    also connected through other hyperedges:
      (none)

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)