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: "solving oscar regularization problems by proximal splitting algorithms"
  Abstract: The OSCAR (octagonal selection and clustering algorithm for regression) regularizer consists of a ‘1 norm plus a pair-wise ‘1 norm (responsible for its grouping behavior) and was proposed to encourage group sparsity in scenarios where the groups are a priori unknown. The OSCAR regularizer has a nontrivial proximity operator, which limits its applicability. We reformulate this regularizer as a weighted sorted ‘1 norm, and propose its grouping proximity operator (GPO) and approximate proximity operator (APO), thus making state-of-the-art proximal splitting algorithms (PSAs) available to solve inverse problems with OSCAR regularization. The GPO is in fact the APO followed by additional grouping and averaging operations, which are costly in time and storage, explaining the reason why algorithms with APO are much faster than that with GPO. The convergences of PSAs with GPO are guaranteed since GPO is an exact proximity operator. Although convergence of PSAs with APO is may not be guaranteed, we have experimentally found that APO behaves similarly to GPO when the regularization parameter of the pair-wise ‘1 norm is set to an appropriately small value. Experiments on recovery of group-sparse signals (with unknown groups) show that PSAs with APO are very fast and accurate.

Connected through hyperedge HE-1 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "convex relaxations of structured matrix factorizations"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"
    - "convex approaches to model wavelet sparsity patterns"

Connected through hyperedge HE-2 (induced by source paper "group sparse matrix recovery"):
  with members:
    - "structured sparsity through convex optimization"
    - "block sparsity coherence and efficient recovery"

Connected through hyperedge HE-3 (induced by source paper "decreasing weighted sorted ell_1 regularization"):
  with members:
    - "structured sparsity through convex optimization"

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)