human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "lime live intrinsic material estimation"
  Abstract: We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill posed inverse rendering problem using recent advances in image to image translation techniques based on deep convolutional encoder decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real world image formation and obtains intermediate results even during run time. The estimation of material parameters at real time frame rates enables exciting mixed reality applications, such as seamless illumination consistent integration of virtual objects into real world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and demonstrate its effectiveness through quantitative and qualitative evaluation.

Connected through hyperedge HE-1 (induced by source paper "deep multi class adversarial specularity removal"):
  with members:
    - "generative collaborative networks for single image super resolution"
    - "live intrinsic material estimation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "specular to diffuse translation for multi view reconstruction"):
  with members:
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "perceptual adversarial networks for image to image transformation"
    - "superpoint self supervised interest point detection and description"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "texturegan controlling deep image synthesis with texture patches"

Connected through hyperedge HE-3 (induced by source paper "deep appearance maps"):
  with members:
    - "live intrinsic material estimation"
    - "single image svbrdf capture with a rendering aware deep network"
    - "deep shading convolutional neural networks for screen space shading"
    - "enhanced deep residual networks for single image super resolution"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "deep outdoor illumination estimation"
    - "what is around the camera"

Question: Please tell me which class the center node 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)