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 "self calibrating deep photometric stereo networks"):
  Abstract: This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of specific reflectances and light source distributions, our method is able to determine both shape and light directions of a scene with unknown arbitrary reflectances observed under unknown varying light directions. To achieve this goal, we propose a two-stage deep learning architecture, called SDPS-Net, which can effectively take advantage of intermediate supervision, resulting in reduced learning difficulty compared to a single-stage model. Experiments on both synthetic and real datasets show that our proposed approach significantly outperforms previous uncalibrated photometric stereo methods.

Includes members:
  Member "neural inverse rendering for general reflectance photometric stereo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "learning to estimate indoor lighting from 3d objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deeplight learning illumination for unconstrained mobile mixed reality")
  Member "cnn ps cnn based photometric stereo for general non convex surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "physics based neural networks for shape from polarization")
  Member "ps fcn a flexible learning framework for photometric stereo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexible svbrdf capture with a multi image deep network")
  Member "learning to predict indoor illumination from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeing the world in a bag of chips")
  Member "label denoising adversarial network ldan for inverse lighting of face images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "copy and paste gan face hallucination from shaded thumbnails")
  Member "silnet single and multi view reconstruction by learning from silhouettes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "deep outdoor illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeing the world in a bag of chips")

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)