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 "physics based neural networks for shape from polarization"):
  Abstract: How should prior knowledge from physics inform a neural network solution? We study the blending of physics and deep learning in the context of Shape from Polarization (SfP). The classic SfP problem recovers an object's shape from polarized photographs of the scene. The SfP problem is special because the physical models are only approximate. Previous attempts to solve SfP have been purely model-based, and are susceptible to errors when real-world conditions deviate from the idealized physics. In our solution, there is a subtlety to combining physics and neural networks. Our final solution blends deep learning with synthetic renderings (derived from physics) in the framework of a two-stage encoder. The lessons learned from this exemplary problem foreshadow the future impact of physics-based learning.

Includes members:
  Member "neural lander stable drone landing control using learned dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "reblur2deblur deblurring videos via self supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised linear motion deblurring")
  Member "inverse transport networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pix2vex image to geometry reconstruction using a smooth differentiable renderer")
  Member "learning a discriminative prior for blind image deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning")
  Member "materials for masses svbrdf acquisition with a single mobile phone image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool")
  Member "cnn ps cnn based photometric stereo for general non convex surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intrinseqnet learning to estimate the reflectance from varying illumination")
  Member "single image svbrdf capture with a rendering aware deep network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "flash photography for data driven hidden scene recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "expertmatcher automating ml model selection for clients using hidden representations")

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)