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: "single image rgb photometric stereo with spatially varying albedo"
  Abstract: We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present a practical and efficient algorithm that uses this model to robustly recover shape from real images. Our method first reasons about shape locally in a dense set of patches in the observed image, producing shape distributions for every patch. These local distributions are then combined to produce a single consistent surface normal map. We demonstrate the efficacy of the approach through experiments on both synthetic renderings as well as real captured images.

Connected through hyperedge HE-1 (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"
    - "led based photometric stereo modeling calibration and numerical solution"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "learning detailed face reconstruction from a single image"
    - "3d face reconstruction by learning from synthetic data"

Connected through hyperedge HE-2 (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"
    - "from shading to local shape"
    - "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-3 (induced by source paper "illuminant spectra based source separation using flash photography"):
  with members:
    - "learning to predict indoor illumination from a single image"
    - "deep outdoor illumination estimation"
    - "convolutional color constancy"
    - "color constancy by learning to predict chromaticity from luminance"

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)