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: "median k flats for hybrid linear modeling with many outliers"
  Abstract: We describe the MedianK-flats (MKF) algorithm, a simple online method for hybrid linear modeling, i.e., for approximating data by a mixture of flats. This algorithm simultaneously partitions the data into clusters while finding their corresponding best approximating l 1  d-flats, so that the cumulative l 1  error is minimized. The current implementation restricts d-flats to be d-dimensional linear subspaces. It requires a negligible amount of storage, and its complexity, when modeling data consisting of N points in ℝD with K d-dimensional linear subspaces, is of order O(n s  · K · d · D + n s  · d2 · D), where n s  is the number of iterations required for convergence (empirically on the order of 104). Since it is an online algorithm, data can be supplied to it incrementally and it can incrementally produce the corresponding output. The performance of the algorithm is carefully evaluated using synthetic and real data.

Connected through hyperedge HE-1 (induced by source paper "robust subspace recovery layer for unsupervised anomaly detection"):
  with members:
    - "robust subspace recovery with adversarial outliers"
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "deep anomaly detection using geometric transformations"
    - "robust pca for anomaly detection in cyber networks"
    - "static and dynamic robust pca and matrix completion a review"
    - "robust deep and inductive anomaly detection"

Connected through hyperedge HE-2 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "automatic subspace learning via principal coefficients embedding"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-3 (induced by source paper "neither global nor local a hierarchical robust subspace clustering for image data"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust and efficient subspace segmentation via least squares regression"
    - "learning transformations for clustering and classification"
    - "robust recovery of subspace structures by low rank representation"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"

Connected through hyperedge HE-4 (induced by source paper "robust subspace clustering by cauchy loss function"):
  with members:
    - "multi view intact space learning"
    - "sce a manifold regularized set covering method for data partitioning"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "a unified framework for representation based subspace clustering of out of sample and large scale data"
    - "joint unsupervised learning of deep representations and image clusters"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-5 (induced by source paper "three stage subspace clustering framework with graph based transformation and optimization"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-6 (induced by source paper "robust subspace recovery with adversarial outliers"):
  with members:
    - "an overview of robust subspace recovery"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "provable self representation based outlier detection in a union of subspaces"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "input sparsity and hardness for robust subspace approximation"
    - "robust computation of linear models by convex relaxation"
    - "optimal algorithms for l_ 1 subspace signal processing"
    - "algorithms and hardness for robust subspace recovery"

Connected through hyperedge HE-7 (induced by source paper "constructing the f graph with a symmetric constraint for subspace clustering"):
  with members:
    - "subspace clustering using a symmetric low rank representation"
    - "locally linear representation for image clustering"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "estimating a manifold from a tangent bundle learner"):
  with members:
    - "tangent based manifold approximation with locally linear models"
    - "learning manifolds with k means and k flats"
    - "learning invariant features through local space contraction"

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)