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: "hyperplane clustering via dual principal component pursuit"
  Abstract: We extend the theoretical analysis of a recently proposed single subspace learning algorithm, called Dual Principal Component Pursuit (DPCP), to the case where the data are drawn from of a union of hyperplanes. To gain insight into the properties of the $\ell_1$ non-convex problem associated with DPCP, we develop a geometric analysis of a closely related continuous optimization problem. Then transferring this analysis to the discrete problem, our results state that as long as the hyperplanes are sufficiently separated, the dominant hyperplane is sufficiently dominant and the points are uniformly distributed inside the associated hyperplanes, then the non-convex DPCP problem has a unique global solution, equal to the normal vector of the dominant hyperplane. This suggests the correctness of a sequential hyperplane learning algorithm based on DPCP. A thorough experimental evaluation reveals that hyperplane learning schemes based on DPCP dramatically improve over the state-of-the-art methods for the case of synthetic data, while are competitive to the state-of-the-art in the case of 3D plane clustering for Kinect data.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "list decodable learning via sum of squares"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"
    - "fourier sparse interpolation without a frequency gap"

Connected through hyperedge HE-3 (induced by source paper "theoretical analysis of sparse subspace clustering with missing entries"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "on deterministic conditions for subspace clustering under missing data"
    - "filtrated algebraic subspace clustering"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"

Connected through hyperedge HE-4 (induced by source paper "efficient mixture model for clustering of sparse high dimensional binary data"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "semi supervised cross entropy clustering with information bottleneck constraint"
    - "innovation pursuit a new approach to subspace clustering"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "why should i trust you explaining the predictions of any classifier"
    - "cross entropy clustering"
    - "entropy of the mixture of sources and entropy dimension"

Connected through hyperedge HE-5 (induced by source paper "dual principal component pursuit"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "provable self representation based outlier detection in a union of subspaces"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "complete dictionary recovery over the sphere"

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)