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: "tracking objects with higher order interactions using delayed column generation"
  Abstract: We study the problem of multi-target tracking and data association in video. We formulate this in terms of selecting a subset of high-quality tracks subject to the constraint that no pair of selected tracks is associated with a common detection (of an object). This objective is equivalent to the classic NP-hard problem of finding a maximum-weight set packing (MWSP) where tracks correspond to sets and is made further difficult since the number of candidate tracks grows exponentially in the number of detections. We present a relaxation of this combinatorial problem that uses a column generation formulation where the pricing problem is solved via dynamic programming to efficiently explore the space of tracks. We employ row generation to tighten the bound in such a way as to preserve efficient inference in the pricing problem. We show the practical utility of this algorithm for tracking problems in natural and biological video datasets.

Connected through hyperedge HE-1 (induced by source paper "efficient global multi object tracking under minimum cost circulation framework"):
  with members:
    - "end to end learning deep crf models for multi object tracking"
    - "a baseline for 3d multi object tracking"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "fusion of head and full body detectors for multi object tracking"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

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"
    - "next generation multicuts for semi planar graphs"
    - "monocular object instance segmentation and depth ordering with cnns"
    - "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"
    - "multi person pose estimation via column generation"
    - "deep image scaling up image recognition"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-4 (induced by source paper "multi person pose estimation via column generation"):
  with members:
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "arttrack articulated multi person tracking in the wild"
    - "moral lineage tracing"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-5 (induced by source paper "exploiting skeletal structure in computer vision annotation with benders decomposition"):
  with members:
    - "efficient pose and cell segmentation using column generation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-6 (induced by source paper "convex optimization for non convex problems via column generation"):
  with members:
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "tensor decompositions for learning latent variable models"
    - "tightening lp relaxations for map using message passing"
    - "a method of moments for mixture models and hidden markov models"
    - "most tensor problems are np hard"

Connected through hyperedge HE-7 (induced by source paper "efficient pose and cell segmentation using column generation"):
  with members:
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "monocular object instance segmentation and depth ordering with cnns"
    - "fast planar correlation clustering for 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)