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: "high dimensional low rank plus sparse matrix decomposition"
  Abstract: This paper is concerned with the problem of low-rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems with complexity that scales with the dimension of the data, which limits their scalability. Furthermore, existing randomized approaches mostly rely on uniform random sampling, which is quite inefficient for many real world data matrices that exhibit additional structures (e.g., clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out by using a small data sketch formed from sampled columns/rows. Even when the data are sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly    $\mathcal {O}(r \mu)$  , where    $\mu$   is the coherency parameter and    $r$   is the rank of the low-rank component. In addition, adaptive sampling algorithms are proposed to address the problem of columns/rows sampling from structured data. We provide an analysis of the proposed method with adaptive sampling and show that adaptive sampling makes the required number of sampled columns/rows invariant to the distribution of the data. The proposed approach is amenable to online implementation and an online scheme is proposed.

Connected through hyperedge HE-1 (induced by source paper "evolutionary self expressive models for subspace clustering"):
  with members:
    - "evolutionary clustering via message passing"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "innovation pursuit a new approach to subspace clustering"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "accelerated sparse subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "compressed randomized utv decompositions for low rank approximations and big data applications"):
  with members:
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "fast randomized singular value thresholding for low rank optimization"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-3 (induced by source paper "scalable and robust community detection with randomized sketching"):
  with members:
    - "spatial random sampling a structure preserving data sketching tool"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "efficient clustering with limited distance information"
    - "community detection via random and adaptive sampling"
    - "community detection thresholds and the weak ramanujan property"
    - "sparse subspace clustering algorithm theory and applications"
    - "clustering partially observed graphs via convex optimization"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "subspace orbit randomized decomposition for low rank matrix approximations"):
  with members:
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "fast randomized singular value thresholding for low rank optimization"
    - "robust subspace clustering"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "coherence pursuit fast simple and robust principal component analysis"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "innovation pursuit a new approach to subspace clustering"
    - "fast algorithms for robust pca via gradient descent"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-6 (induced by source paper "spatial random sampling a structure preserving data sketching tool"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"
    - "robust and scalable column row sampling from corrupted big data"
    - "column selection via adaptive sampling"
    - "provable deterministic leverage score sampling"
    - "greedy column subset selection for large scale data sets"
    - "optimal column based low rank matrix reconstruction"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-7 (induced by source paper "multi view task driven recognition in visual sensor networks"):
  with members:
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "control allocation for wide area coordinated damping"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "feature encoding in band limited distributed surveillance systems"
    - "quality based multimodal classification using tree structured sparsity"

Connected through hyperedge HE-8 (induced by source paper "compressive pca for low rank matrices on graphs"):
  with members:
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "network topology identification from spectral templates"
    - "compressive spectral clustering"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"
    - "random sampling of bandlimited signals on graphs"

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.NA(Numerical Analysis)