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: "a dictionary based approach for estimating shape and spatially varying reflectance"
  Abstract: We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF. We assume that multiple images of the object are obtained under fixed view-point and varying illumination, i.e, the setting of photometric stereo. Assuming that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary, we derive a per-pixel surface normal and BRDF estimation framework that requires neither iterative optimization techniques nor careful initialization, both of which are endemic to most state-of-the-art techniques. We showcase the performance of our technique on a wide range of simulated and real scenes where we outperform competing methods.

Connected through hyperedge HE-1 (induced by source paper "materials for masses svbrdf acquisition with a single mobile phone image"):
  with members:
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "material editing using a physically based rendering network"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "a lightweight approach for on the fly reflectance estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-2 (induced by source paper "ps fcn a flexible learning framework for photometric stereo"):
  with members:
    - "learned multi patch similarity"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "adam a method for stochastic optimization"
    - "designing deep networks for surface normal estimation"
    - "depth map prediction from a single image using a multi scale deep network"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "robust photometric stereo via dictionary learning"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "single image rgb photometric stereo with spatially varying albedo"
    - "from shading to local shape"
    - "efficient sum of outer products dictionary learning soup dil the ell_0 method"
    - "efficient sum of sparse outer products dictionary learning soup dil"

Connected through hyperedge HE-4 (induced by source paper "shape and spatially varying reflectance estimation from virtual exemplars"):
  with members:
    - "robust principal component analysis"

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)