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 "recovery of noisy points on band limited surfaces kernel methods re explained"):
  Abstract: We introduce a continuous domain framework for the recovery of points on a surface in high dimensional space, represented as the zero-level set of a bandlimited function. We show that the exponential maps of the points on the surface satisfy annihilation relations, implying that they lie in a finite dimensional subspace. The subspace properties are used to derive sampling conditions, which will guarantee the perfect recovery of the surface from finite number of points. We rely on nuclear norm minimization to exploit the low-rank structure of the maps to recover the points from noisy measurements. Since the direct estimation of the surface is computationally prohibitive in very high dimensions, we propose an iterative reweighted algorithm using the "kernel trick". The iterative algorithm reveals deep links to Laplacian based algorithms widely used in graph signal processing; the theory and the sampling conditions can serve as a basis for discrete-continuous domain processing of signals on a graph.

Includes members:
  Member "recovery of damped exponentials using structured low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "calibration free b0 correction of epi data using structured low rank matrix recovery")
  Member "convex recovery of continuous domain piecewise constant images from non uniform fourier samples":
    also connected through other hyperedges:
      (none)
  Member "novel structured low rank algorithm to recover spatially smooth exponential image time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "calibration free b0 correction of epi data using structured low rank matrix recovery")
  Member "a fast algorithm for convolutional structured low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")
  Member "recovery of piecewise smooth images from few fourier samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convex recovery of continuous domain piecewise constant images from non uniform fourier samples")
  Member "off the grid recovery of piecewise constant images from few fourier samples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")

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.CV(Computer Vision and Pattern Recognition)