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: "super resolution off the grid"
  Abstract: Super-resolution is the problem of recovering a superposition of point sources using bandlimited measurements, which may be corrupted with noise. This signal processing problem arises in numerous imaging problems, ranging from astronomy to biology to spectroscopy, where it is common to take (coarse) Fourier measurements of an object. Of particular interest is in obtaining estimation procedures which are robust to noise, with the following desirable statistical and computational properties: we seek to use coarse Fourier measurements (bounded by some cutoff frequency); we hope to take a (quantifiably) small number of measurements; we desire our algorithm to run quickly. #R##N#Suppose we have k point sources in d dimensions, where the points are separated by at least \Delta from each other (in Euclidean distance). This work provides an algorithm with the following favorable guarantees: - The algorithm uses Fourier measurements, whose frequencies are bounded by O(1/\Delta) (up to log factors). Previous algorithms require a cutoff frequency which may be as large as {\Omega}( d/\Delta). - The number of measurements taken by and the computational complexity of our algorithm are bounded by a polynomial in both the number of points k and the dimension d, with no dependence on the separation \Delta. In contrast, previous algorithms depended inverse polynomially on the minimal separation and exponentially on the dimension for both of these quantities. #R##N#Our estimation procedure itself is simple: we take random bandlimited measurements (as opposed to taking an exponential number of measurements on the hyper-grid). Furthermore, our analysis and algorithm are elementary (based on concentration bounds for sampling and the singular value decomposition).

Connected through hyperedge HE-1 (induced by source paper "a fast algorithm for separated sparsity via perturbed lagrangians"):
  with members:
    - "approximation algorithms for model based compressive sensing"
    - "structured sparsity through convex optimization"
    - "sampling and recovery of pulse streams"
    - "structured compressed sensing from theory to applications"
    - "model based compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "fourier sparse interpolation without a frequency gap"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "near minimax line spectral estimation"
    - "low rank approximation and regression in input sparsity time"

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)