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: "non linear subspace clustering with learned low rank kernels"
  Abstract: In this paper, we present a kernel subspace clustering method that can handle non-linear models. In contrast to recent kernel subspace clustering methods which use predefined kernels, we propose to learn a low-rank kernel matrix, with which mapped data in feature space are not only low-rank but also self-expressive. In this manner, the low-dimensional subspace structures of the (implicitly) mapped data are retained and manifested in the high-dimensional feature space. We evaluate the proposed method extensively on both motion segmentation and image clustering benchmarks, and obtain superior results, outperforming the kernel subspace clustering method that uses standard kernels[Patel 2014] and other state-of-the-art linear subspace clustering methods.

Connected through hyperedge HE-1 (induced by source paper "self supervised convolutional subspace clustering network"):
  with members:
    - "constrained sparse subspace clustering with side information"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "adaptive low rank kernel subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "scalable deep k subspace clustering"):
  with members:
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "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"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "matrix recovery with implicitly low rank data"):
  with members:
    - "non linear dimensionality regularizer for solving inverse problems"
    - "robust principal component analysis on graphs"
    - "robust recovery of subspace structures by low rank representation"
    - "robust pca via outlier pursuit"
    - "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)