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 "lighthouse predicting lighting volumes for spatially coherent illumination"):
  Abstract: We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images.

Includes members:
  Member "neural illumination lighting prediction for indoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeing the world in a bag of chips")
  Member "fast spatially varying indoor lighting estimation":
    also connected through other hyperedges:
      (none)
  Member "neural inverse rendering of an indoor scene from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "putting humans in a scene learning affordance in 3d indoor environments")
  Member "inverse rendering for complex indoor scenes shape spatially varying lighting and svbrdf from a single image":
    also connected through other hyperedges:
      (none)
  Member "local light field fusion practical view synthesis with prescriptive sampling guidelines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seeing the world in a bag of chips")
  Member "neural volumes learning dynamic renderable volumes from images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nerf representing scenes as neural radiance fields for view synthesis")
  Member "deeplight learning illumination for unconstrained mobile mixed reality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeing the world in a bag of chips")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)