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 class model fitting by energy minimization and mode seeking"
  Abstract: We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting - the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used alpha-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a cross-validation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the state-of-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting.

Connected through hyperedge HE-1 (induced by source paper "consac robust multi model fitting by conditional sample consensus"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "progressive x efficient anytime multi model fitting algorithm"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "learning to find good correspondences"

Connected through hyperedge HE-2 (induced by source paper "learning for multi type subspace clustering"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "deep subspace clustering networks"
    - "convolutional neural network architecture for geometric matching"
    - "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-3 (induced by source paper "learning for multi model and multi type fitting"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "deep subspace clustering networks"
    - "convolutional neural network architecture for geometric matching"
    - "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-4 (induced by source paper "robust motion segmentation from pairwise matches"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "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-5 (induced by source paper "a new method for atlanta world frame estimation"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "robust and globally optimal manhattan frame estimation in near real time"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-6 (induced by source paper "progressive x efficient anytime multi model fitting algorithm"):
  with members:
    - "searching for representative modes on hypergraphs for robust geometric model fitting"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "graph cut ransac"
    - "mode seeking on hypergraphs for robust geometric model fitting"

Connected through hyperedge HE-7 (induced by source paper "optimal multi view correction of local affine frames"):
  with members:
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "radially distorted conjugate translations"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-8 (induced by source paper "primitive fitting using deep boundary aware geometric segmentation"):
  with members:
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "casenet deep category aware semantic edge detection"
    - "de noising stabilizing and completing 3d reconstructions on the go using plane priors"
    - "real time 3d scene description using spheres cones and cylinders"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)