human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection"):
  Abstract: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary $m$-dimensional sparse vector $\mathbf{x}$ having $k$ nonzero entries from $n$ measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing. Existing greedy-based algorithms achieve optimal $n = \mathcal{O}(k\log\frac{m}{k})$ sampling complexity with computational complexity that is linear in the size of the data $m$ and cardinality constraint $k$. However, the ${\mathcal{O}}(mk)$ computational complexity is still prohibitive for large-scale datasets. In this paper, we present the first sparse support selection algorithm for arbitrary sparse vectors that achieves exact identification of the optimal subset from $n = \mathcal{O}(k\log\frac{m}{k})$ measurements with $\tilde{\mathcal{O}}(m)$ computational complexity. The proposed scheme utilizes the idea of randomly restricting search space of the greedy method in a progressive manner to reduce the computational cost while maintaining the same order of sampling complexity as the existing greedy schemes. Simulation results including an application of the algorithm to the task of column subset selection demonstrate efficacy of the proposed algorithm.

Includes members:
  Member "scalable sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "greedy column subset selection for large scale data sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multi criteria approach for fast and outlier aware representative selection from manifolds")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis")
  Member "optimal column based low rank matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "near optimal column based matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressive sensing for cut improvement and local clustering")

Question: Please tell me which class the source paper 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.DM(Discrete Mathematics)