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 "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"):
  Abstract: In this work, we provide non-asymptotic, probabilistic guarantees for successful sparse support recovery by the Multiple Sparse Bayesian Learning (M-SBL) algorithm in the multiple measurement vector (MMV) framework. For joint sparse Gaussian sources, we show that M-SBL can perfectly recover their common nonzero support with arbitrarily high probability using only finitely many MMVs. In fact, the support error probability decays exponentially fast with the number of MMVs, with the decay rate depending on the restricted isometry constant of the self Khatri-Rao product of the measurement matrix. Our analysis theoretically confirms that M-SBL can indeed recover supports of size up to $\mathcal{O}(m^{2})$, where $m$ is the number of measurements per MMV. This is in contrast to popular MMV algorithms in compressed sensing such as simultaneous orthogonal matching pursuit and group LASSO, which have been shown to succeed only when the sparsity is at most $\mathcal{O}(m)$. Next, in the presence of measurement noise, we show that M-SBL can recover the true $k$-sparse support of $n$-dimensional joint sparse Gaussian sources using at most $\mathcal{O}(k \log{n})$ MMVs, while only a single MMV suffices in the noiseless case. Unlike existing support recovery guarantees for M-SBL, our sufficient conditions are truly non-asymptotic and do not require the orthogonality of the nonzero rows of the joint sparse signals.

Includes members:
  Member "on the restricted isometry of the columnwise khatri rao product":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "joint sparse recovery method for compressed sensing with structured dictionary mismatches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse methods for direction of arrival estimation")
  Member "estimation of sparse mimo channels with common support":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "subspace methods for joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "support recovery of sparse signals in the presence of multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint sparse recovery using signal space matching pursuit")
  Member "theoretical and empirical results for recovery from multiple measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "distributed compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

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)