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: "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"
  Abstract: We introduce a novel method to obtain high-quality 3D reconstructions from consumer RGB-D sensors. Our core idea is to simultaneously optimize for geometry encoded in a signed distance field (SDF), textures from automatically-selected keyframes, and their camera poses along with material and scene lighting. To this end, we propose a joint surface reconstruction approach that is based on Shape-from-Shading (SfS) techniques and utilizes the estimation of spatially-varying spherical harmonics (SVSH) from subvolumes of the reconstructed scene. Through extensive examples and evaluations, we demonstrate that our method dramatically increases the level of detail in the reconstructed scene geometry and contributes highly to consistent surface texture recovery.

Connected through hyperedge HE-1 (induced by source paper "routedfusion learning real time depth map fusion"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "surfelmeshing online surfel based mesh reconstruction"
    - "mesh r cnn"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-2 (induced by source paper "fast spatially varying indoor lighting estimation"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "deep sky modeling for single image outdoor lighting estimation"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "taskonomy disentangling task transfer learning"
    - "learning to estimate indoor lighting from 3d objects"
    - "dynamic environment mapping for augmented reality applications on mobile devices"
    - "learning to predict indoor illumination from a single image"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "3d appearance super resolution with deep learning"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "benchmarking super resolution algorithms on real data"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "inverse path tracing for joint material and lighting estimation"):
  with members:
    - "inverse transport networks"
    - "single image svbrdf capture with a rendering aware deep network"
    - "neural 3d mesh renderer"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "specular to diffuse translation for multi view reconstruction"):
  with members:
    - "lime live intrinsic material estimation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning free form deformations for 3d object reconstruction"):
  with members:
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "compact model representation for 3d reconstruction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"

Connected through hyperedge HE-7 (induced by source paper "a differential volumetric approach to multi view photometric stereo"):
  with members:
    - "led based photometric stereo modeling calibration and numerical solution"

Connected through hyperedge HE-8 (induced by source paper "variational reflectance estimation from multi view images"):
  with members:
    - "led based photometric stereo modeling calibration and numerical solution"

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)