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: "ps fcn a flexible learning framework for photometric stereo"
  Abstract: This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo.Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.

Connected through hyperedge HE-1 (induced by source paper "flexible svbrdf capture with a multi image deep network"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "deep sets"
    - "single image svbrdf capture with a rendering aware deep network"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "material editing using a physically based rendering network"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-2 (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"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "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-3 (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"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "densely connected convolutional networks"

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)