human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "the minimum cost connected subgraph problem in medical image analysis"
  Abstract: Several important tasks in medical image analysis can be stated in the form of an optimization problem whose feasible solutions are connected subgraphs. Examples include the reconstruction of neural or vascular structures under connectedness constraints.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for moral lineage tracing"):
  with members:
    - "arttrack articulated multi person tracking in the wild"
    - "moral lineage tracing"
    - "lifting of multicuts"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "fast planar correlation clustering for image segmentation"
    - "large scale correlation clustering optimization"

Connected through hyperedge HE-2 (induced by source paper "an ilp solver for multi label mrfs with connectivity constraints"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "unbiased shape compactness for segmentation"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

Connected through hyperedge HE-3 (induced by source paper "joint graph decomposition and node labeling by local search"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "mot16 a benchmark for multi object tracking"
    - "end to end instance segmentation and counting with recurrent attention"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "end to end instance segmentation with recurrent attention"
    - "instance aware semantic segmentation via multi task network cascades"
    - "proposal free network for instance level object segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Question: Please tell me which class the center node 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)