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: "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
  Abstract: We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise covariance matrix is a common model for a sparse array of sensors consisted of several widely separated subarrays. Since interelement spacing among sensors in a subarray is small, the noise in the subarray is in general spatially correlated, while, due to large distances between subarrays, the noise between them is uncorrelated. Consequently, the noise covariance matrix of such an array has a block diagonal structure which is indeed sparse. Moreover, in an ordinary nonsparse array, because of small distance between adjacent sensors, there is noise coupling between neighboring sensors, whereas one can assume that non-adjacent sensors have spatially uncorrelated noise which makes again the array noise covariance matrix sparse. Utilizing some recently available tools in low-rank/sparse matrix decomposition, matrix completion, and sparse representation, we propose a novel method which can resolve possibly correlated or even coherent sources in the aforementioned partly correlated noise. In particular, when the sources are uncorrelated, our approach involves solving a second-order cone programming (SOCP), and if they are correlated or coherent, one needs to solve a computationally harder convex program. We demonstrate the effectiveness of the proposed algorithm by numerical simulations and comparison to the Cramer-Rao bound (CRB).

Connected through hyperedge HE-1 (induced by source paper "non iterative subspace based doa estimation in the presence of nonuniform noise"):
  with members:
    - "joint detection and localization of an unknown number of sources using the algebraic structure of the noise subspace"

Connected through hyperedge HE-2 (induced by source paper "successive concave sparsity approximation for compressed sensing"):
  with members:
    - "iterative concave rank approximation for recovering low rank matrices"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"):
  with members:
    - "sparse signal recovery by ell_q minimization under restricted isometry property"
    - "calibration using matrix completion with application to ultrasound tomography"
    - "new ric bounds via l_q minimization with 0 q 1 in compressed sensing"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "on ric bounds of compressed sensing matrices for approximating sparse solutions using l q quasi norms"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "matrix completion with noise"
    - "sparse recovery by non convex optimization instance optimality"

Connected through hyperedge HE-4 (induced by source paper "iterative concave rank approximation for recovering low rank matrices"):
  with members:
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "calibration using matrix completion with application to ultrasound tomography"
    - "matrix completion with noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "exact matrix completion via convex optimization"

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)