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: "is matching pursuit solving convex problems"
  Abstract: Sparse recovery ({\tt SR}) has emerged as a very powerful tool for signal processing, data mining and pattern recognition. To solve {\tt SR}, many efficient matching pursuit (\texttt{MP}) algorithms have been proposed. However, it is still not clear whether {\tt SR} can be formulated as a convex problem that is solvable using \texttt{MP} algorithms. To answer this, in this paper, a novel convex relaxation model is presented, which is solved by a general matching pursuit (\texttt{GMP}) algorithm under the convex programming framework. {\tt GMP} has several advantages over existing methods. At first, it solves a convex problem and guarantees to converge to a optimum. In addition, with $\ell_1$-regularization, it can recover any $k$-sparse signals if the restricted isometry constant $\sigma_k\leq 0.307-\nu$, where $\nu$ can be arbitrarily close to 0. Finally, when dealing with a batch of signals, the computation burden can be much reduced using a batch-mode \texttt{GMP}. Comprehensive numerical experiments show that \texttt{GMP} achieves better performance than other methods in terms of sparse recovery ability and efficiency. We also apply \texttt{GMP} to face recognition tasks on two well-known face databases, namely, \emph{{Extended using}} and \emph{AR}. Experimental results demonstrate that {\tt GMP} can achieve better recognition performance than the considered state-of-the-art methods within acceptable time. {Particularly, the batch-mode {\tt GMP} can be up to 500 times faster than the considered $\ell_1$ methods.}

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)