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: "cnn ps cnn based photometric stereo for general non convex surfaces"
  Abstract: Most conventional photometric stereo algorithms inversely solve a BRDF-based image formation model. However, the actual imaging process is often far more complex due to the global light transport on the non-convex surfaces. This paper presents a photometric stereo network that directly learns relationships between the photometric stereo input and surface normals of a scene. For handling unordered, arbitrary number of input images, we merge all the input data to the intermediate representation called {\it observation map} that has a fixed shape, is able to be fed into a CNN. To improve both training and prediction, we take into account the rotational pseudo-invariance of the observation map that is derived from the isotropic constraint. For training the network, we create a synthetic photometric stereo dataset that is generated by a physics-based renderer, therefore the global light transport is considered. Our experimental results on both synthetic and real datasets show that our method outperforms conventional BRDF-based photometric stereo algorithms especially when scenes are highly non-convex.

Connected through hyperedge HE-1 (induced by source paper "physics based neural networks for shape from polarization"):
  with members:
    - "neural lander stable drone landing control using learned dynamics"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "inverse transport networks"
    - "learning a discriminative prior for blind image deblurring"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "single image svbrdf capture with a rendering aware deep network"
    - "flash photography for data driven hidden scene recovery"
    - "physics based generative adversarial models for image restoration and beyond"

Connected through hyperedge HE-2 (induced by source paper "intrinseqnet learning to estimate the reflectance from varying illumination"):
  with members:
    - "live intrinsic material estimation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "learning intrinsic image decomposition from watching the world"
    - "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-3 (induced by source paper "self calibrating deep photometric stereo networks"):
  with members:
    - "neural inverse rendering for general reflectance photometric stereo"
    - "learning to estimate indoor lighting from 3d objects"
    - "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-4 (induced by source paper "spline net sparse photometric stereo through lighting interpolation and normal estimation networks"):
  with members:
    - "self calibrating deep photometric stereo networks"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "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-5 (induced by source paper "photometric depth super resolution"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "a variational approach to shape from shading under natural illumination"
    - "learning to predict indoor illumination from a single image"
    - "led based photometric stereo modeling calibration and numerical solution"
    - "variational methods for normal integration"
    - "neural face editing with intrinsic image disentangling"
    - "normal integration a survey"
    - "learning non lambertian object intrinsics across shapenet categories"

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)