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: "randomized hybrid linear modeling by local best fit flats"
  Abstract: The hybrid linear modeling problem is to identify a set of d-dimensional affine sets in RD. It arises, for example, in object tracking and structure from motion. The hybrid linear model can be considered as the second simplest (behind linear) manifold model of data. In this paper we will present a very simple geometric method for hybrid linear modeling based on selecting a set of local best fit flats that minimize a global l 1  error measure. The size of the local neighborhoods is determined automatically by the Jones' β 2  numbers; it is proven under certain geometric conditions that good local neighborhoods exist and are found by our method. We also demonstrate how to use this algorithm for fast determination of the number of affine subspaces. We give extensive experimental evidence demonstrating the state of the art accuracy and speed of the algorithm on synthetic and real hybrid linear data.

Connected through hyperedge HE-1 (induced by source paper "a new approach to two view motion segmentation using global dimension minimization"):
  with members:
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "hybrid linear modeling via local best fit flats"
    - "a geometric analysis of subspace clustering with outliers"
    - "kernel spectral curvature clustering kscc"
    - "median k flats for hybrid linear modeling with many outliers"

Connected through hyperedge HE-2 (induced by source paper "hybrid linear modeling via local best fit flats"):
  with members:
    - "generalized principal component analysis gpca"
    - "a geometric analysis of subspace clustering with outliers"
    - "motion segmentation by scc on the hopkins 155 database"
    - "median k flats for hybrid linear modeling with many outliers"

Connected through hyperedge HE-3 (induced by source paper "a geometric analysis of subspace clustering with outliers"):
  with members:
    - "generalized principal component analysis gpca"
    - "hybrid linear modeling via local best fit flats"
    - "exact subspace segmentation and outlier detection by low rank representation"
    - "median k flats for hybrid linear modeling with many outliers"

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)