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 "fast and near optimal matrix completion via randomized basis pursuit"):
  Abstract: Motivated by the philosophy and phenomenal success of com- pressed sensing, the problem of reconstructing a matrix from a sampling of its entries has attracted much attention recently. Such a problem can be viewed as an information-theoretic variant of the well-studied matrix completion prob- lem, and the main objective is to design an efficient algorithm that can re- construct a matrix by inspecting only a small number of its entries. Although this is an impossible task in general, Candes and co-authors have recently shown that under a so-called incoherence assumption, a rank rn × n matrix can be reconstructed using semidefinite programming (SDP) after one inspects O(nr log 6 n) of its entries. In this paper we propose an alternative approach that is much more efficient and can reconstruct a larger class of matrices by inspecting a significantly smaller number of the entries. Specifically, we first introduce a class of matrices, which we call stable matrices, and show that it includes all those that satisfy the incoherence assumption. Then, we propose a randomized basis pursuit (RBP) algorithm and show that it can reconstruct a stable rank rn × n matrix after inspecting O(nr log n) of its entries. Our sam- pling bound is only a logarithmic factor away from the information-theoretic limit and is essentially optimal. Moreover, the runtime of the RBP algorithm is bounded by O(nr 2 log n + n 2 r), which compares very favorably with the Ω(n 4 r 2 log 12 n) runtime of the SDP-based algorithm. Perhaps more impor- tantly, our algorithm will provide an exact reconstruction of the input matrix in strongly polynomial time if the matrix entries are rational. By contrast, the SDP-based algorithm can only provide an approximate one in polynomial time.

Includes members:
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "uniqueness of low rank matrix completion by rigidity theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "matrix completion with noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "exact matrix completion via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "relative error cur matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")

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.IT(Information Theory)