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: "the fast bilateral solver"
  Abstract: We present the bilateral solver, a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms. Our technique is capable of matching or improving upon state-of-the-art results on several different computer vision tasks (stereo, depth superresolution, colorization, and semantic segmentation) while being 10-1000 times faster than competing approaches. The bilateral solver is fast, robust, straightforward to generalize to new domains, and simple to integrate into deep learning pipelines.

Connected through hyperedge HE-1 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "boundary cues for 3d object shape recovery"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "deep ordinal regression network for monocular depth estimation"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "evaluation of cnn based single image depth estimation methods"

Connected through hyperedge HE-2 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "learned collaborative stereo refinement"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "deepmvs learning multi view stereopsis"
    - "continuous 3d label stereo matching using local expansion moves"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"

Connected through hyperedge HE-3 (induced by source paper "pyramidal edge maps based guided thermal super resolution"):
  with members:
    - "keyframe based direct thermal inertial odometry"
    - "multimodal sensor fusion in single thermal image super resolution"
    - "multimedia super resolution via deep learning a survey"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "richer convolutional features for edge detection"
    - "fully convolutional siamese networks for object tracking"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"

Connected through hyperedge HE-5 (induced by source paper "learning task specific generalized convolutions in the permutohedral lattice"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic filtering with large sampling field for convnets"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"
    - "segmentation aware convolutional networks using local attention masks"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (induced by source paper "pixel adaptive convolutional neural networks"):
  with members:
    - "image transformer"
    - "convolutional crfs for semantic segmentation"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fast end to end trainable guided filter"
    - "segmentation aware convolutional networks using local attention masks"
    - "non local neural networks"
    - "attention is all you need"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-7 (induced by source paper "deformable kernel networks for joint image filtering"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "learning guided convolutional network for depth completion"
    - "blade filter learning for general purpose computational photography"
    - "video frame interpolation via adaptive convolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "burst denoising with kernel prediction networks"
    - "joint image filtering with deep convolutional networks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-8 (induced by source paper "guided image to image translation with bi directional feature transformation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"
    - "free form image inpainting with gated convolution"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)