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: "a new approach to two view motion segmentation using global dimension minimization"
  Abstract: We present a new approach to rigid-body motion segmentation from two views. We use a previously developed nonlinear embedding of two-view point correspondences into a 9-dimensional space and identify the different motions by segmenting lower-dimensional subspaces. In order to overcome nonuniform distributions along the subspaces, whose dimensions are unknown, we suggest the novel concept of global dimension and its minimization for clustering subspaces with some theoretical motivation. We propose a fast projected gradient algorithm for minimizing global dimension and thus segmenting motions from 2-views. We develop an outlier detection framework around the proposed method, and we present state-of-the-art results on outlier-free and outlier-corrupted two-view data for segmenting motion.

Connected through hyperedge HE-1 (induced by source paper "motion segmentation using locally affine atom voting"):
  with members:
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "motion segmentation by scc on the hopkins 155 database"

Connected through hyperedge HE-2 (induced by source paper "superpixel guided two view deterministic geometric model fitting"):
  with members:
    - "hypergraph modelling for geometric model fitting"
    - "superpixel based two view deterministic fitting for multiple structure data"
    - "mode seeking on hypergraphs for robust geometric model fitting"
    - "deterministic fitting of multiple structures using iterative maxfs with inlier scale estimation"

Connected through hyperedge HE-3 (induced by source paper "effective sampling fast segmentation using robust geometric model fitting"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "hybrid linear modeling via local best fit flats"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "deep sparse subspace clustering"):
  with members:
    - "deep subspace clustering networks"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "facenet a unified embedding for face recognition and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-5 (induced by source paper "better feature tracking through subspace constraints"):
  with members:
    - "sparse subspace clustering algorithm theory and applications"
    - "hybrid linear modeling via local best fit flats"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

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)