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 "learning to estimate indoor lighting from 3d objects"):
  Abstract: In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object. To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps. This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods. To achieve this, our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting. Our second contribution is a convolutional neural network that predicts the light from a single image of a known object. To train these networks, our third contribution is a novel dataset that contains 21,000 HDR indoor environment maps. The results indicate that the predictor can generate plausible lighting estimations even from diffuse objects.

Includes members:
  Member "learning to predict indoor illumination from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeing the world in a bag of chips")
  Member "deep 6 dof tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "material editing using a physically based rendering network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset")
  Member "matterport3d learning from rgb d data in indoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an exploration of embodied visual exploration")
  Member "learning a predictable and generative vector representation for objects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "deep outdoor illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing the world in a bag of chips")
  Member "what is around the camera":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeing the world in a bag of chips")
  Member "delight net decomposing reflectance maps into specular materials and natural illumination":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural inverse rendering of an indoor scene from a single image")

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)