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: "motion segmentation by exploiting complementary geometric models"
  Abstract: Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation would lead to difficulty. Even when we are confronted with a general scene-motion, the fundamental matrix approach as a model for motion segmentation still suffers from several defects, which we discuss in this paper. The full potential of the fundamental matrix approach could only be realized if we judiciously harness information from the simpler homography model. From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together. We show that the performance can be substantially improved in this way. We perform extensive testing on existing motion segmentation datasets, achieving state-of-the-art performance on all of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing real-world effects such as strong perspectives and strong forward translations not seen in the traditional datasets.

Connected through hyperedge HE-1 (induced by source paper "learning for multi type subspace clustering"):
  with members:
    - "deep subspace clustering networks"
    - "convolutional neural network architecture for geometric matching"
    - "multi class model fitting by energy minimization and mode seeking"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to find good correspondences"
    - "nonnegative matrix underapproximation for robust multiple model fitting"
    - "deep image homography estimation"

Connected through hyperedge HE-2 (induced by source paper "learning for multi model and multi type fitting"):
  with members:
    - "deep subspace clustering networks"
    - "convolutional neural network architecture for geometric matching"
    - "multi class model fitting by energy minimization and mode seeking"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to find good correspondences"
    - "deep image homography estimation"
    - "deep metric learning via facility location"

Connected through hyperedge HE-3 (induced by source paper "robust motion segmentation from pairwise matches"):
  with members:
    - "multi class model fitting by energy minimization and mode seeking"
    - "a survey of structure from motion"
    - "spectral synchronization of multiple views in se 3"
    - "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
    - "on constrained spectral clustering and its applications"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "stereo based multi motion visual odometry for mobile robots"):
  with members:
    - "effective sampling fast segmentation using robust geometric model fitting"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "moving object tracking employing rigid body motion on matrix lie groups"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mot16 a benchmark for multi object tracking"
    - "direct sparse odometry"

Connected through hyperedge HE-5 (induced by source paper "3d rigid motion segmentation with mixed and unknown number of models"):
  with members:
    - "higher order minimum cost lifted multicuts for motion segmentation"
    - "a detailed rubric for motion segmentation"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "robust video background identification by dominant rigid motion estimation"):
  with members:
    - "it s moving a probabilistic model for causal motion segmentation in moving camera videos"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

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)