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: "compressive optical deflectometric tomography a constrained total variation minimization approach"
  Abstract: Optical Deflectometric Tomography (ODT) provides an accurate characterization of transparent materials whose complex surfaces present a real challenge for manufacture and control. In ODT, the refractive index map (RIM) of a transparent object is reconstructed by measuring light deflection under multiple orientations. We show that this imaging modality can be made "compressive", i.e., a correct RIM reconstruction is achievable with far less observations than required by traditional Filtered Back Projection (FBP) methods. Assuming a cartoon-shape RIM model, this reconstruction is driven by minimizing the map Total-Variation under a fidelity constraint with the available observations. Moreover, two other realistic assumptions are added to improve the stability of our approach: the map positivity and a frontier condition. Numerically, our method relies on an accurate ODT sensing model and on a primal-dual minimization scheme, including easily the sensing operator and the proposed RIM constraints. We conclude this paper by demonstrating the power of our method on synthetic and experimental data under various compressive scenarios. In particular, the compressiveness of the stabilized ODT problem is demonstrated by observing a typical gain of 20 dB compared to FBP at only 5% of 360 incident light angles for moderately noisy sensing.

Connected through hyperedge HE-1 (induced by source paper "the importance of phase in complex compressive sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "uniform recovery of fusion frame structured sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "on cartesian line sampling with anisotropic total variation regularization"):
  with members:
    - "compressed sensing with structured sparsity and structured acquisition"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "towards a mathematical theory of super resolution"
    - "stable and robust sampling strategies for compressive imaging"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "non parametric psf estimation from celestial transit solar images using blind deconvolution"):
  with members:
    - "compressive imaging and characterization of sparse light deflection maps"

Connected through hyperedge HE-4 (induced by source paper "generalized inpainting method for hyperspectral image acquisition"):
  with members:
    - "a parallel compressive imaging architecture for one shot acquisition"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "spread spectrum magnetic resonance imaging"
    - "theory and applications of compressed sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-6 (induced by source paper "stable and robust sampling strategies for compressive imaging"):
  with members:
    - "an analysis of block sampling strategies in compressed sensing"
    - "spread spectrum magnetic resonance imaging"
    - "stable image reconstruction using total variation minimization"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "on variable density compressive sampling"
    - "a probabilistic and ripless theory of compressed sensing"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-7 (induced by source paper "from bits to images inversion of local binary descriptors"):
  with members:
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "compressive schlieren deflectometry"):
  with members:
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"

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.CV(Computer Vision and Pattern Recognition)