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 "learning dense voxel embeddings for 3d neuron reconstruction"):
  Abstract: We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A metric graph on an arbitrary set of short and long-range edges can be constructed from the dense embeddings generated by a convolutional network. Partitioning the metric graph with long-range affinities as repulsive constraints can produce an initial segmentation with high precision, with substantial improvements on very thin objects. The convolutional embedding net is reused without any modification to agglomerate the systematic splits caused by complex "self-touching"' objects. Our proposed method achieves state-of-the-art accuracy on the challenging problem of 3D neuron reconstruction from the brain images acquired by serial section electron microscopy. Our alternative, object-centered representation could be more generally useful for other computational tasks in automated neural circuit reconstruction.

Includes members:
  Member "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "where are the masks instance segmentation with image level supervision")
  Member "single image piece wise planar 3d reconstruction via associative embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "learning metric graphs for neuron segmentation in electron microscopy images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation":
    also connected through other hyperedges:
      (none)
  Member "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images")
  Member "superhuman accuracy on the snemi3d connectomics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non local u net for biomedical image segmentation")

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)