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: "hybrid linear modeling via local best fit flats"
  Abstract: We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e., subspaces approximating the data in local neighborhoods. The correct sizes of the local neighborhoods are determined automatically by the Jones' β 2 numbers (we prove under certain geometric conditions that our method finds the optimal local neighborhoods). The collection of subspaces is further processed by a greedy selection procedure or a spectral method to generate the final model. We discuss applications to tracking-based motion segmentation and clustering of faces under different illuminating conditions. We give extensive experimental evidence demonstrating the state of the art accuracy and speed of the suggested algorithms on these problems and also on synthetic hybrid linear data as well as the MNIST handwritten digits data; and we demonstrate how to use our algorithms for fast determination of the number of affine subspaces.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "neural collaborative subspace clustering"):
  with members:
    - "scalable deep k subspace clustering"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep subspace clustering networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "adaptive low rank kernel subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-4 (induced by source paper "restricted connection orthogonal matching pursuit for sparse subspace clustering"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "algebraic clustering of affine subspaces"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-5 (induced by source paper "an overview of robust subspace recovery"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "static and dynamic robust pca via low rank sparse matrix decomposition a review"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "estimation of camera locations in highly corrupted scenarios all about that base no shape trouble"
    - "static and dynamic robust pca and matrix completion a review"
    - "being robust in high dimensions can be practical"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "a well tempered landscape for non convex robust subspace recovery"

Connected through hyperedge HE-6 (induced by source paper "scaled simplex representation for subspace clustering"):
  with members:
    - "ell_0 motivated low rank sparse subspace clustering"
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "discriminative regression machine a classifier for high dimensional data or imbalanced data"
    - "clustering with similarity preserving"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "discriminative transformation learning for fuzzy sparse subspace clustering"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-7 (induced by source paper "scalable deep k subspace clustering"):
  with members:
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "robust multi body feature tracker a segmentation free approach"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-8 (induced by source paper "effective sampling fast segmentation using robust geometric model fitting"):
  with members:
    - "a new approach to two view motion segmentation using global dimension minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"

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)