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 "the mutex watershed and its objective efficient parameter free image partitioning"):
  Abstract: Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose a greedy algorithm for signed graph partitioning, the "Mutex Watershed". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.

Includes members:
  Member "learned watershed end to end learning of seeded segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "patchperpix for instance segmentation")
  Member "superhuman accuracy on the snemi3d connectomics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non local u net for biomedical image segmentation")
  Member "learning steerable filters for rotation equivariant cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive group equivariant convolutional networks")
  Member "anisotropic em segmentation by 3d affinity learning and agglomeration":
    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 "multi stage multi recursive input fully convolutional networks for neuronal boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boundary aware feature propagation for scene segmentation")
  Member "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "a multi cut formulation for joint segmentation and tracking of multiple objects":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention")
  Member "3d u net learning dense volumetric segmentation from sparse annotation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional occupancy networks")

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)