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 "robust photometric stereo via dictionary learning"):
  Abstract: Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources. While effective in some situations, classical photometric stereo relies on a diffuse surface model that cannot handle objects with complex reflectance patterns, and it is sensitive to non-idealities in the images. In this work, we propose a novel approach to photometric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Specifically, we develop three formulations for applying dictionary learning to photometric stereo. We propose a preprocessing step that utilizes dictionary learning to denoise the images. We also present a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model. Finally, we generalize the latter model to explicitly model non-Lambertian objects. We investigate all three approaches through extensive experimentation on synthetic and real benchmark datasets and observe state-of-the-art performance compared to existing robust photometric stereo methods.

Includes members:
  Member "efficient sum of outer products dictionary learning soup dil and its application to inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of fast structured dictionary learning")
  Member "single image rgb photometric stereo with spatially varying albedo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d face reconstruction using color photometric stereo with uncalibrated near point lights")
  Member "from shading to local shape":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structural decompositions for end to end relighting")
  Member "efficient sum of outer products dictionary learning soup dil the ell_0 method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient sum of outer products dictionary learning soup dil and its application to inverse problems")
  Member "a dictionary based approach for estimating shape and spatially varying reflectance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "materials for masses svbrdf acquisition with a single mobile phone image")
  Member "efficient sum of sparse outer products dictionary learning soup dil":
    also connected through other hyperedges:
      (none)

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)