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 low rank subspace segmentation with semidefinite guarantees"
  Abstract: Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group){Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data such as those (approximately) lying on subspaces {We follow {liu2010robust} and use the term "subspace" to denote both linear subspaces and affine subspaces. There is a trivial conversion between linear subspaces and affine subspaces as mentioned therein.} or low-dimensional manifolds. By learning the affinity matrix in the form of sparse reconstruction, techniques proposed in this vein often considerably boost the performance in subspace settings where traditional SC can fail. Despite the success, there are fundamental problems that have been left unsolved: the spectrum property of the learned affinity matrix cannot be gauged in advance, and there is often one ugly symmetrization step that post-processes the affinity for SC input. Hence we advocate to enforce the symmetric positive semidefinite constraint explicitly during learning (Low-Rank Representation with Positive SemiDefinite constraint, or LRR-PSD), and show that factually it can be solved in an exquisite scheme efficiently instead of general-purpose SDP solvers that usually scale up poorly. We provide rigorous mathematical derivations to show that, in its canonical form, LRR-PSD is equivalent to the recently proposed Low-Rank Representation (LRR) scheme {liu2010robust}, and hence offer theoretic and practical insights to both LRR-PSD and LRR, inviting future research. As per the computational cost, our proposal is at most comparable to that of LRR, if not less. We validate our theoretic analysis and optimization scheme by experiments on both synthetic and real data sets.

Connected through hyperedge HE-1 (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"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-2 (induced by source paper "symmetric low rank representation for subspace clustering"):
  with members:
    - "subspace clustering using a symmetric low rank representation"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (induced by source paper "correlation adaptive subspace segmentation by trace lasso"):
  with members:
    - "robust and efficient subspace segmentation via least squares regression"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "trace lasso a trace norm regularization for correlated designs"

Connected through hyperedge HE-4 (induced by source paper "relations among some low rank subspace recovery models"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"
    - "a geometric analysis of subspace clustering with outliers"
    - "algorithms and hardness for robust subspace recovery"
    - "randomized algorithms for matrices and data"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-5 (induced by source paper "fixed rank representation for unsupervised visual learning"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "a geometric analysis of subspace clustering with outliers"
    - "exact subspace segmentation and outlier detection by low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "rank norm regularization with closed form solutions application to subspace clustering"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "closed form solutions to a category of nuclear norm minimization problems"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "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)