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: "neural inverse rendering for general reflectance photometric stereo"
  Abstract: We present a novel convolutional neural network architecture for photometric stereo (Woodham, 1980), a problem of recovering 3D object surface normals from multiple images observed under varying illuminations. Despite its long history in computer vision, the problem still shows fundamental challenges for surfaces with unknown general reflectance properties (BRDFs). Leveraging deep neural networks to learn complicated reflectance models is promising, but studies in this direction are very limited due to difficulties in acquiring accurate ground truth for training and also in designing networks invariant to permutation of input images. In order to address these challenges, we propose a physics based unsupervised learning framework where surface normals and BRDFs are predicted by the network and fed into the rendering equation to synthesize observed images. The network weights are optimized during testing by minimizing reconstruction loss between observed and synthesized images. Thus, our learning process does not require ground truth normals or even pre-training on external images. Our method is shown to achieve the state-of-the-art performance on a challenging real-world scene benchmark.

Connected through hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems"):
  with members:
    - "pro cam ssfm projector camera system for structure and spectral reflectance from motion"
    - "compennet end to end full projector compensation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "self calibrating deep photometric stereo networks"
    - "end to end projector photometric compensation"
    - "a single shot per pose camera projector calibration system for imperfect planar targets"
    - "inverserendernet learning single image inverse rendering"
    - "deep learning the latent space of light transport"

Connected through hyperedge HE-2 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "learned collaborative stereo refinement"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "deepmvs learning multi view stereopsis"
    - "continuous 3d label stereo matching using local expansion moves"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "deep geometric prior for surface reconstruction"

Connected through hyperedge HE-3 (induced by source paper "neural inverse rendering of an indoor scene from a single image"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning intrinsic image decomposition from watching the world"
    - "inverserendernet learning single image inverse rendering"
    - "inverse transport networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-4 (induced by source paper "intrinseqnet learning to estimate the reflectance from varying illumination"):
  with members:
    - "live intrinsic material estimation"
    - "learning intrinsic image decomposition from watching the world"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "self supervised intrinsic image decomposition"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "self calibrating deep photometric stereo networks"):
  with members:
    - "learning to estimate indoor lighting from 3d objects"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "ps fcn a flexible learning framework for photometric stereo"
    - "learning to predict indoor illumination from a single image"
    - "label denoising adversarial network ldan for inverse lighting of face images"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "deep outdoor illumination estimation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "spline net sparse photometric stereo through lighting interpolation and normal estimation networks"):
  with members:
    - "self calibrating deep photometric stereo networks"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "ps fcn a flexible learning framework for photometric stereo"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "rendernet a deep convolutional network for differentiable rendering from 3d shapes"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "deep shading convolutional neural networks for screen space shading"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"

Connected through hyperedge HE-8 (induced by source paper "cnn ps cnn based photometric stereo for general non convex surfaces"):
  with members:
    - "learning a multi view stereo machine"
    - "sfm net learning of structure and motion from video"
    - "a lightweight approach for on the fly reflectance estimation"
    - "densely connected convolutional networks"
    - "shapenet an information rich 3d model repository"
    - "shape and spatially varying reflectance estimation from virtual exemplars"
    - "adam a method for stochastic optimization"

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)