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: "led based photometric stereo modeling calibration and numerical solution"
  Abstract: We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be directional, which is very difficult to achieve in practice. Rather, we use light-emitting diodes (LEDs) to illuminate the scene to reconstruct. Such point light sources are very convenient to use, yet they yield a more complex photometric stereo model which is arduous to solve. We first derive in a physically sound manner this model, and show how to calibrate its parameters. Then, we discuss two state-of-the-art numerical solutions. The first one alternatingly estimates the albedo and the normals, and then integrates the normals into a depth map. It is shown empirically to be independent from the initialization, but convergence of this sequential approach is not established. The second one directly recovers the depth, by formulating photometric stereo as a system of PDEs which are partially linearized using image ratios. Although the sequential approach is avoided, initialization matters a lot and convergence is not established either. Therefore, we introduce a provably convergent alternating reweighted least-squares scheme for solving the original system of PDEs, without resorting to image ratios for linearization. Finally, we extend this study to the case of RGB images.

Connected through hyperedge HE-1 (induced by source paper "inferring super resolution depth from a moving light source enhanced rgb d sensor a variational approach"):
  with members:
    - "on the well posedness of uncalibrated photometric stereo under general lighting"
    - "photometric depth super resolution"
    - "a differential volumetric approach to multi view photometric stereo"
    - "detailed surface geometry and albedo recovery from rgb d video under natural illumination"
    - "normal integration a survey"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "3d face reconstruction using color photometric stereo with uncalibrated near point lights"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "single image rgb photometric stereo with spatially varying albedo"
    - "learning detailed face reconstruction from a single image"
    - "3d face reconstruction by learning from synthetic data"

Connected through hyperedge HE-3 (induced by source paper "photometric depth super resolution"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "a variational approach to shape from shading under natural illumination"
    - "learning to predict indoor illumination from a single image"
    - "variational methods for normal integration"
    - "neural face editing with intrinsic image disentangling"
    - "normal integration a survey"
    - "learning non lambertian object intrinsics across shapenet categories"

Connected through hyperedge HE-4 (induced by source paper "a differential volumetric approach to multi view photometric stereo"):
  with members:
    - "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"

Connected through hyperedge HE-5 (induced by source paper "optimisation of photometric stereo methods by non convex variational minimisation"):
  with members:
    - "photometric stereo by hemispherical metric embedding"
    - "photometric stereo for strong specular highlights"
    - "fast and accurate surface normal integration on non rectangular domains"

Connected through hyperedge HE-6 (induced by source paper "variational reflectance estimation from multi view images"):
  with members:
    - "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"

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)