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 unified framework for representation based subspace clustering of out of sample and large scale data"
  Abstract: Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph, which describes the neighborhood relations among data points. Some recent works build the graph using sparse, low-rank, and    $\ell _{2}$   -norm-based representation, and have achieved the state-of-the-art performance. However, these methods have suffered from the following two limitations. First, the time complexities of these methods are at least proportional to the cube of the data size, which make those methods inefficient for solving the large-scale problems. Second, they cannot cope with the out-of-sample data that are not used to construct the similarity graph. To cluster each out-of-sample datum, the methods have to recalculate the similarity graph and the cluster membership of the whole data set. In this paper, we propose a unified framework that makes the representation-based subspace clustering algorithms feasible to cluster both the out-of-sample and the large-scale data. Under our framework, the large-scale problem is tackled by converting it as the out-of-sample problem in the manner of sampling, clustering, coding, and classifying. Furthermore, we give an estimation for the error bounds by treating each subspace as a point in a hyperspace. Extensive experimental results on various benchmark data sets show that our methods outperform several recently proposed scalable methods in clustering a large-scale data set.

Connected through hyperedge HE-1 (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"
    - "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"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "connections between nuclear norm and frobenius norm based representations"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "learning locality constrained collaborative representation for robust face recognition"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "collaborative representation based classification for face recognition"
    - "sparse subspace clustering algorithm theory and applications"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-3 (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"
    - "symmetric low rank representation for subspace clustering"
    - "fast low rank representation based spatial pyramid matching for image classification"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "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.LG(Machine Learning)