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: "innovation pursuit a new approach to subspace clustering"
  Abstract: In subspace clustering, a group of data points belonging to a union of subspaces are assigned membership to their respective subspaces. This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties. We present two frameworks in which the idea of innovation pursuit is used to distinguish the subspaces. Underlying the first framework is an iterative method that finds the subspaces consecutively by solving a series of simple linear optimization problems, each searching for a direction of innovation in the span of the data potentially orthogonal to all subspaces except for the one to be identified in one step of the algorithm. A detailed mathematical analysis is provided establishing sufficient conditions for iPursuit to correctly cluster the data. The proposed approach can provably yield exact clustering even when the subspaces have significant intersections. It is shown that the complexity of the iterative approach scales only linearly in the number of data points and subspaces, and quadratically in the dimension of the subspaces. The second framework integrates iPursuit with spectral clustering to yield a new variant of spectral-clustering-based algorithms. The numerical simulations with both real and synthetic data demonstrate that iPursuit can often outperform the state-of-the-art subspace clustering algorithms, more so for subspaces with significant intersections, and that it significantly improves the state-of-the-art result for subspace-segmentation-based face clustering.

Connected through hyperedge HE-1 (induced by source paper "minimal sample subspace learning theory and algorithms"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "robust subspace clustering via smoothed rank approximation"
    - "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"
    - "comparing measures of sparsity"

Connected through hyperedge HE-2 (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"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "accelerated sparse subspace clustering"

Connected through hyperedge HE-3 (induced by source paper "coherence pursuit fast simple and robust principal component analysis"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "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-4 (induced by source paper "spatial random sampling a structure preserving data sketching tool"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "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-5 (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"
    - "hyperplane clustering via dual principal component pursuit"
    - "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-6 (induced by source paper "a direction search and spectral clustering based approach to subspace clustering"):
  with members:
    - "spatial random sampling a structure preserving data sketching tool"
    - "robust computation of linear models by convex relaxation"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"

Connected through hyperedge HE-7 (induced by source paper "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"):
  with members:
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "feature encoding in band limited distributed surveillance systems"
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "sparse modeling for image and vision processing"
    - "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"

Connected through hyperedge HE-8 (induced by source paper "robust and scalable column row sampling from corrupted big data"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "dissimilarity based sparse subset selection"

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)