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: "calibration using matrix completion with application to ultrasound tomography"
  Abstract: We study the application of matrix completion in the process of calibrating physical devices. In particular we propose an algorithm together with reconstruction bounds for calibrating circular ultrasound tomography devices. We use the time-of-flight (ToF) measurements between sensor pairs in a homogeneous medium to calibrate the system. The calibration process consists of a low-rank matrix completion algorithm to de-noise and estimate random and structured missing ToFs, and the classic multi-dimensional scaling method to estimate the sensor positions from the ToF measurements. We provide theoretical bounds on the calibration error. Several simulations are conducted to evaluate the theoretical results presented in this paper.

Connected through hyperedge HE-1 (induced by source paper "localization from incomplete euclidean distance matrix performance analysis for the svd mds approach"):
  with members:
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-2 (induced by source paper "euclidean distance matrices essential theory algorithms and applications"):
  with members:
    - "phase retrieval for sparse signals uniqueness conditions"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "the distance geometry of music"

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"
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "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:
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "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"
    - "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.LG(Machine Learning)