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: "virtual rephotography novel view prediction error for 3d reconstruction"
  Abstract: The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.

Connected through hyperedge HE-1 (induced by source paper "understanding the limitations of cnn based absolute camera pose regression"):
  with members:
    - "lf net learning local features from images"
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "improved visual relocalization by discovering anchor points"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-2 (induced by source paper "deepmvs learning multi view stereopsis"):
  with members:
    - "self normalizing neural networks"
    - "learning a multi view stereo machine"
    - "learned multi patch similarity"
    - "demon depth and motion network for learning monocular stereo"
    - "playing for benchmarks"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-3 (induced by source paper "learning to predict image based rendering artifacts with respect to a hidden reference image"):
  with members:
    - "gans nqm a generative adversarial networks based no reference quality assessment metric for rgb d synthesized views"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "on the use of deep learning for blind image quality assessment"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "surface light field fusion"):
  with members:
    - "radiometric scene decomposition scene reflectance illumination and geometry from rgb d images"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "multi scale context aggregation by dilated convolutions"
    - "material recognition in the wild with the materials in context database"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "submodular trajectory optimization for aerial 3d scanning"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "autonomous exploration with a low cost quadrocopter using semi dense monocular slam"
    - "learning to gather information via imitation"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "fast semidifferential based submodular function optimization"
    - "efficient informative sensing using multiple robots"

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)