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: "correntropy induced l2 graph for robust subspace clustering"
  Abstract: In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different regularization of the representation coefficient. We instead focus on the robustness of the model to non-Gaussian noises. We propose a new robust clustering method by using the correntropy induced metric, which is robust for handling the non-Gaussian and impulsive noises. Also we further extend the method for handling the data with outlier rows/features. The multiplicative form of half-quadratic optimization is used to optimize the non-convex correntropy objective function of the proposed models. Extensive experiments on face datasets well demonstrate that the proposed methods are more robust to corruptions and occlusions.

Connected through hyperedge HE-1 (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"
    - "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"
    - "greedy feature selection for subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "active orthogonal matching pursuit for sparse subspace clustering"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "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-3 (induced by source paper "subspace clustering by block diagonal representation"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "guess who rated this movie identifying users through subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-4 (induced by source paper "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "noisy subspace clustering via matching pursuits"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "sssc am a unified framework for video co segmentation by structured sparse subspace clustering with appearance and motion features"
    - "convex sparse spectral clustering single view to multi view"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-5 (induced by source paper "subspace clustering using a symmetric low rank representation"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "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"
    - "symmetric low rank representation for subspace clustering"
    - "fast low rank representation based spatial pyramid matching for image classification"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "multi view surveillance video summarization via joint embedding and sparse optimization"):
  with members:
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "video summarization in a multi view camera network"
    - "learning from multiple sources for video summarisation"
    - "active object localization with deep reinforcement learning"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "collaborative summarization of topic related videos"):
  with members:
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "video summarization in a multi view camera network"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "kernel truncated regression representation for robust subspace clustering"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "locally linear representation for image clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

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)