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: "multi person pose estimation via column generation"
  Abstract: We study the problem of multi-person pose estimation in natural images. A pose estimate describes the spatial position and identity (head, foot, knee, etc.) of every non-occluded body part of a person. Pose estimation is difficult due to issues such as deformation and variation in body configurations and occlusion of parts, while multi-person settings add complications such as an unknown number of people, with unknown appearance and possible interactions in their poses and part locations. We give a novel integer program formulation of the multi-person pose estimation problem, in which variables correspond to assignments of parts in the image to poses in a two-tier, hierarchical way. This enables us to develop an efficient custom optimization procedure based on column generation, where columns are produced by exact optimization of very small scale integer programs. We demonstrate improved accuracy and speed for our method on the MPII multi-person pose estimation benchmark.

Connected through hyperedge HE-1 (induced by source paper "accelerating message passing for map with benders decomposition"):
  with members:
    - "efficient multi person pose estimation with provable guarantees"
    - "exploiting skeletal structure in computer vision annotation with benders decomposition"
    - "deepercut a deeper stronger and faster multi person pose estimation model"

Connected through hyperedge HE-2 (induced by source paper "efficient column generation for cell detection and segmentation"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "next generation multicuts for semi planar graphs"
    - "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 "efficient multi person pose estimation with provable guarantees"):
  with members:
    - "exploiting skeletal structure in computer vision annotation with benders decomposition"
    - "deep image scaling up image recognition"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "tracking objects with higher order interactions using delayed column generation"

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)