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 "1 eps approximate sparse recovery"):
  Abstract: The problem central to sparse recovery and compressive sensing is that of stable sparse recovery: we want a distribution of matrices A in R^{m\times n} such that, for any x \in R^n and with probability at least 2/3 over A, there is an algorithm to recover x* from Ax with #R##N#||x* - x||_p   1 and norm p. The measurement complexity of this problem is well understood for constant C > 1. However, in a variety of applications it is important to obtain C = 1 + eps for a small eps > 0, and this complexity is not well understood. We resolve the dependence on eps in the number of measurements required of a k-sparse recovery algorithm, up to polylogarithmic factors for the central cases of p = 1 and p = 2. Namely, we give new algorithms and lower bounds that show the number of measurements required is (1/eps^{p/2})k polylog(n). For p = 2, our bound of (1/eps) k log(n/k) is tight up to constant factors. We also give matching bounds when the output is required to be k-sparse, in which case we achieve (1/eps^p) k polylog(n). This shows the distinction between the complexity of sparse and non-sparse outputs is fundamental.

Includes members:
  Member "information equals amortized communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication for generating correlation a unifying survey")
  Member "how well can we estimate a sparse vector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "querying a matrix through matrix vector products")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random access for massive machine type communications")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy preserving learning via deep net pruning")

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.DS(Data Structures and Algorithms)