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: "from shading to local shape"
  Abstract: We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch. This produces a mid-level scene descriptor, comprised of local shape distributions that are inferred separately at every image patch across multiple scales. The framework is based on a quadratic representation of local shape that, in the absence of noise, has guarantees on recovering accurate local shape and lighting. And when noise is present, the inferred local shape distributions provide useful shape information without over-committing to any particular image explanation. These local shape distributions naturally encode the fact that some smooth diffuse regions are more informative than others, and they enable efficient and robust reconstruction of object-scale shape. Experimental results show that this approach to surface reconstruction compares well against the state-of-art on both synthetic images and captured photographs.

Connected through hyperedge HE-1 (induced by source paper "structural decompositions for end to end relighting"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "synthetic depth of field with a single camera mobile phone"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to predict indoor illumination from a single image"
    - "neural face editing with intrinsic image disentangling"
    - "shading annotations in the wild"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-2 (induced by source paper "learning to reconstruct texture less deformable surfaces from a single view"):
  with members:
    - "geometry aware network for non rigid shape prediction from a single view"
    - "neural face editing with intrinsic image disentangling"
    - "self supervised intrinsic image decomposition"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "learning detailed face reconstruction from a single image"
    - "template based monocular 3d shape recovery using laplacian meshes"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-3 (induced by source paper "shape from shading through shape evolution"):
  with members:
    - "playing for benchmarks"
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "a large dataset of object scans"
    - "playing for data ground truth from computer games"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-4 (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"
    - "efficient sum of outer products dictionary learning soup dil the ell_0 method"
    - "a dictionary based approach for estimating shape and spatially varying reflectance"
    - "efficient sum of sparse outer products dictionary learning soup dil"

Connected through hyperedge HE-5 (induced by source paper "tensors differential geometry and statistical shading analysis"):
  with members:
    - "what s in a patch i tensors differential geometry and statistical shading analysis"
    - "critical contours an invariant linking image flow with salient surface organization"

Connected through hyperedge HE-6 (induced by source paper "robust surface reconstruction from gradients via adaptive dictionary regularization"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil the ell_0 method"

Connected through hyperedge HE-7 (induced by source paper "robust photometric stereo using learned image and gradient dictionaries"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil the ell_0 method"

Connected through hyperedge HE-8 (induced by source paper "solving uncalibrated photometric stereo using fewer images by jointly optimizing low rank matrix completion and integrability"):
  with members:
    - "partial sum minimization of singular values in robust pca algorithm and applications"

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)