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: "robust multiple manifolds structure learning"
  Abstract: We present a robust multiple manifolds structure learning (RMMSL) scheme to robustly estimate data structures under the multiple low intrinsic dimensional manifolds assumption. In the local learning stage, RMMSL efficiently estimates local tangent space by weighted low-rank matrix factorization. In the global learning stage, we propose a robust manifold clustering method based on local structure learning results. The proposed clustering method is designed to get the flattest manifolds clusters by introducing a novel curved-level similarity function. Our approach is evaluated and compared to state-of-the-art methods on synthetic data, handwritten digit images, human motion capture data and motorbike videos. We demonstrate the effectiveness of the proposed approach, which yields higher clustering accuracy, and produces promising results for challenging tasks of human motion segmentation and motion flow learning from videos.

Connected through hyperedge HE-1 (induced by source paper "a multi criteria approach for fast and outlier aware representative selection from manifolds"):
  with members:
    - "salient object detection a discriminative regional feature integration approach"
    - "dissimilarity based sparse subset selection"
    - "greedy column subset selection for large scale data sets"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-2 (induced by source paper "a tube and droplet based approach for representing and analyzing motion trajectories"):
  with members:
    - "a heat map based algorithm for recognizing group activities in videos"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "riemannian geometry based modeling and clustering of network wide non stationary time series the brain network case"):
  with members:
    - "a geometric analysis of subspace clustering with outliers"

Connected through hyperedge HE-4 (induced by source paper "on clustering and embedding manifolds using a low rank neighborhood approach"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "trace lasso a trace norm regularization for correlated designs"
    - "exact matrix completion via convex optimization"

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)