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 "efficient algorithms for moral lineage tracing"):
  Abstract: Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task. Jug et al. have proposed a mathematical abstraction of this task, the moral lineage tracing problem (MLTP) whose feasible solutions define a segmentation of every image and a lineage forest of cells. Their branch-and-cut algorithm, however, is prone to many cuts and slow convergences for large instances. To address this problem, we make three contributions: Firstly, we improve the branch-and-cut algorithm by separating tighter cutting planes. Secondly, we define two primal feasible local search algorithms for the MLTP. Thirdly, we show in experiments that our algorithms decrease the runtime on the problem instances of Jug et al. considerably and find solutions on larger instances in reasonable time.

Includes members:
  Member "the minimum cost connected subgraph problem in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an ilp solver for multi label mrfs with connectivity constraints")
  Member "arttrack articulated multi person tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "moral lineage tracing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cell detection with star convex polygons")
  Member "lifting of multicuts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the mutex watershed and its objective efficient parameter free image partitioning")
  Member "efficient decomposition of image and mesh graphs by lifted multicuts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "fast planar correlation clustering for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation")
  Member "large scale correlation clustering optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "digital technologies in airport ground operations")

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)