human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "intrinseqnet learning to estimate the reflectance from varying illumination"):
  Abstract: Intrinsic image decomposition describes an image based on its reflectance and shading components. In this paper we tackle the problem of estimating the diffuse reflectance from a sequence of images captured from a fixed viewpoint under various illuminations. To this end we propose a deep learning approach to avoid heuristics and strong assumptions on the reflectance prior. We compare two network architectures: one classic 'U' shaped Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) composed of Convolutional Gated Recurrent Units (CGRU). We train our networks on a new dataset specifically designed for the task of intrinsic decomposition from sequences. We test our networks on MIT and BigTime datasets and outperform state-of-the-art algorithms both qualitatively and quantitatively.

Includes members:
  Member "live intrinsic material estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pix2vex image to geometry reconstruction using a smooth differentiable renderer")
  Member "neural inverse rendering for general reflectance photometric stereo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "learning intrinsic image decomposition from watching the world":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "implicit generation and generalization in energy based models")
  Member "cnn ps cnn based photometric stereo for general non convex surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "physics based neural networks for shape from polarization")
  Member "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "self supervised intrinsic image decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion")
  Member "cnn based learning using reflection and retinex models for intrinsic image decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "shadingnet image intrinsics by fine grained shading decomposition")
  Member "learning data driven reflectance priors for intrinsic image decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset")

Question: Please tell me which class the source paper 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)