human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through 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"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"

Connected through hyperedge HE-2 (induced by source paper "grant free massive random access with a massive mimo receiver"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the restricted isometry property of centered self khatri rao products"
    - "polar coding and random spreading for unsourced multiple access"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "on the restricted isometry of the columnwise khatri rao product"

Connected through hyperedge HE-3 (induced by source paper "on the restricted isometry of the columnwise khatri rao product"):
  with members:
    - "graph sampling for covariance estimation"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "smoothed analysis of tensor decompositions"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "hidden cliques and the certification of the restricted isometry property"
    - "new bounds for restricted isometry constants"

Question: Please tell me which class the center node 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)