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: "geometric multi model fitting with a convex relaxation algorithm"
  Abstract: We propose a novel method to fit and segment multi-structural data via convex relaxation. Unlike greedy methods --which maximise the number of inliers-- this approach efficiently searches for a soft assignment of points to models by minimising the energy of the overall classification. Our approach is similar to state-of-the-art energy minimisation techniques which use a global energy. However, we deal with the scaling factor (as the number of models increases) of the original combinatorial problem by relaxing the solution. This relaxation brings two advantages: first, by operating in the continuous domain we can parallelize the calculations. Second, it allows for the use of different metrics which results in a more general formulation. #R##N#We demonstrate the versatility of our technique on two different problems of estimating structure from images: plane extraction from RGB-D data and homography estimation from pairs of images. In both cases, we report accurate results on publicly available datasets, in most of the cases outperforming the state-of-the-art.

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"
    - "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"
    - "multi class model fitting by energy minimization and mode seeking"
    - "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 "a new method for atlanta world frame estimation"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "multi class model fitting by energy minimization and mode seeking"
    - "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-3 (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"
    - "multi class model fitting by energy minimization and mode seeking"
    - "graph cut ransac"
    - "mode seeking on hypergraphs for robust geometric model fitting"

Connected through hyperedge HE-4 (induced by source paper "occlusion robust mvo multimotion estimation through occlusion via motion closure"):
  with members:
    - "the oxford multimotion dataset multiple se 3 motions with ground truth"
    - "a white noise on jerk motion prior for continuous time trajectory estimation on se 3"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"

Connected through hyperedge HE-5 (induced by source paper "fast regularity constrained plane reconstruction"):
  with members:
    - "searching for representative modes on hypergraphs for robust geometric model fitting"
    - "latent ransac"
    - "robust and globally optimal manhattan frame estimation in near real time"

Connected through hyperedge HE-6 (induced by source paper "primitive fitting using deep boundary aware geometric segmentation"):
  with members:
    - "multi class model fitting by energy minimization and mode seeking"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"):
  with members:
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "mot16 a benchmark for multi object tracking"

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)