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: "a lightweight approach for on the fly reflectance estimation"
  Abstract: Estimating surface reflectance (BRDF) is one key component for complete 3D scene capture, with wide applications in virtual reality, augmented reality, and human computer interaction. Prior work is either limited to controlled environments (\eg gonioreflectometers, light stages, or multi-camera domes), or requires the joint optimization of shape, illumination, and reflectance, which is often computationally too expensive (\eg hours of running time) for real-time applications. Moreover, most prior work requires HDR images as input which further complicates the capture process. In this paper, we propose a lightweight approach for surface reflectance estimation directly from $8$-bit RGB images in real-time, which can be easily plugged into any 3D scanning-and-fusion system with a commodity RGBD sensor. Our method is learning-based, with an inference time of less than 90ms per scene and a model size of less than 340K bytes. We propose two novel network architectures, HemiCNN and Grouplet, to deal with the unstructured input data from multiple viewpoints under unknown illumination. We further design a loss function to resolve the color-constancy and scale ambiguity. In addition, we have created a large synthetic dataset, SynBRDF, which comprises a total of $500$K RGBD images rendered with a physically-based ray tracer under a variety of natural illumination, covering $5000$ materials and $5000$ shapes. SynBRDF is the first large-scale benchmark dataset for reflectance estimation. Experiments on both synthetic data and real data show that the proposed method effectively recovers surface reflectance, and outperforms prior work for reflectance estimation in uncontrolled environments.

Connected through hyperedge HE-1 (induced by source paper "a similarity measure for material appearance"):
  with members:
    - "learning icons appearance similarity"
    - "on the convergence of adam and beyond"
    - "brdf estimation of complex materials with nested learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "recognizing material properties from images"
    - "revisiting temporal modeling for video based person reid"
    - "gaussian material synthesis"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-2 (induced by source paper "photoshape photorealistic materials for large scale shape collections"):
  with members:
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "photographic image synthesis with cascaded refinement networks"
    - "image to image translation with conditional adversarial networks"
    - "playing for data ground truth from computer games"
    - "differential angular imaging for material recognition"

Connected through hyperedge HE-3 (induced by source paper "materials for masses svbrdf acquisition with a single mobile phone image"):
  with members:
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "material editing using a physically based rendering network"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "shapenet an information rich 3d model repository"
    - "deep reflectance maps"

Connected through hyperedge HE-4 (induced by source paper "cnn ps cnn based photometric stereo for general non convex surfaces"):
  with members:
    - "neural inverse rendering for general reflectance photometric stereo"
    - "learning a multi view stereo machine"
    - "sfm net learning of structure and motion from video"
    - "densely connected convolutional networks"
    - "shapenet an information rich 3d model repository"
    - "shape and spatially varying reflectance estimation from virtual exemplars"
    - "adam a method for stochastic optimization"

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)