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: "next generation multicuts for semi planar graphs"
  Abstract: We study the problem of multicut segmentation. We introduce modified versions of the Semi-PlanarCC based on bounding Lagrange multipliers. We apply our work to natural image segmentation.

Connected through hyperedge HE-1 (induced by source paper "massively parallel benders decomposition for correlation clustering"):
  with members:
    - "accelerating message passing for map with benders decomposition"
    - "exploiting skeletal structure in computer vision annotation with benders decomposition"
    - "a message passing algorithm for the minimum cost multicut problem"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "parallel correlation clustering on big graphs"
    - "fast planar correlation clustering for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "efficient column generation for cell detection and segmentation"):
  with members:
    - "multi person pose estimation via column generation"
    - "u net convolutional networks for biomedical image segmentation"
    - "monocular object instance segmentation and depth ordering with cnns"
    - "tracking objects with higher order interactions using delayed column generation"
    - "fast planar correlation clustering for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "tracking objects with higher order interactions using delayed column generation"):
  with members:
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "fast planar correlation clustering for image segmentation"
    - "tightening lp relaxations for map using message passing"

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)