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 "subspace methods for joint sparse recovery"):
  Abstract: We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, which consists of the jointly sparse signals, has linearly independent nonzero rows. In this case, the MUltiple SIgnal Classification (MUSIC) algorithm, originally proposed by Schmidt for the direction of arrival estimation problem in sensor array processing and later proposed and analyzed for joint sparse recovery by Feng and Bresler, provides a guarantee with the minimum number of measurements. We focus instead on the unfavorable but practically significant case of rank defect or ill-conditioning. This situation arises with a limited number of measurement vectors, or with highly correlated signal components. In this case, MUSIC fails and, in practice, none of the existing methods can consistently approach the fundamental limit. We propose subspace-augmented MUSIC (SA-MUSIC), which improves on MUSIC such that the support is reliably recovered under such unfavorable conditions. Combined with a subspace-based greedy algorithm, known as Orthogonal Subspace Matching Pursuit, which is also proposed and analyzed in this paper, SA-MUSIC provides a computationally efficient algorithm with a performance guarantee. The performance guarantees are given in terms of a version of the restricted isometry property. In particular, we also present a non-asymptotic perturbation analysis of the signal subspace estimation step, which has been missing in the previous studies of MUSIC.

Includes members:
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "compressive music a missing link between compressive sensing and array signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a compact formulation for the ell_ 2 1 mixed norm minimization problem")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "theoretical and empirical results for recovery from multiple measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image guided depth sampling and reconstruction")
  Member "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "frame moments and welch bound with erasures")

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)