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: "efficient sparse subspace clustering by nearest neighbour filtering"
  Abstract: Sparse Subspace Clustering (SSC) has been used extensively for subspace identification tasks due to its theoretical guarantees and relative ease of implementation. However SSC has quadratic computation and memory requirements with respect to the number of input data points. This burden has prohibited SSCs use for all but the smallest datasets. To overcome this we propose a new method, k-SSC, that screens out a large number of data points to both reduce SSC to linear memory and computational requirements. We provide theoretical analysis for the bounds of success for k-SSC. Our experiments show that k-SSC exceeds theoretical expectations and outperforms existing SSC approximations by maintaining the classification performance of SSC. Furthermore in the spirit of reproducible research we have publicly released the source code for k-SSC

Connected through hyperedge HE-1 (induced by source paper "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "provable deterministic leverage score sampling"
    - "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"

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)