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 "photorealistic material editing through direct image manipulation"):
  Abstract: Creating photorealistic materials for light transport algorithms requires carefully fine-tuning a set of material properties to achieve a desired artistic effect. This is typically a lengthy process that involves a trained artist with specialized knowledge. In this work, we present a technique that aims to empower novice and intermediate-level users to synthesize high-quality photorealistic materials by only requiring basic image processing knowledge. In the proposed workflow, the user starts with an input image and applies a few intuitive transforms (e.g., colorization, image inpainting) within a 2D image editor of their choice, and in the next step, our technique produces a photorealistic result that approximates this target image. Our method combines the advantages of a neural network-augmented optimizer and an encoder neural network to produce high-quality output results within 30 seconds. We also demonstrate that it is resilient against poorly-edited target images and propose a simple extension to predict image sequences with a strict time budget of 1-2 seconds per image.

Includes members:
  Member "deepred deep image prior powered by red":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block coordinate regularization by denoising")
  Member "analyzing inverse problems with invertible neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "materials for masses svbrdf acquisition with a single mobile phone image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool")
  Member "single image svbrdf capture with a rendering aware deep network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "gaussian material synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a similarity measure for material appearance")
  Member "modeling surface appearance from a single photograph using self augmented convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "neural optimizer search with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.GR(Graphics)