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: "deep surface normal estimation with hierarchical rgb d fusion"
  Abstract: The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding. However, as a fundamental scene understanding task, surface normal estimation from RGB-D data lacks thorough investigation. In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image. Specifically, the features from color image and depth are successively integrated at multiple scales to ensure global surface smoothness while preserving visually salient details. Meanwhile, the depth features are re-weighted with a confidence map estimated from depth before merging into the color branch to avoid artifacts caused by input depth corruption. Additionally, a hybrid multi-scale loss function is designed to learn accurate normal estimation given noisy ground-truth dataset. Extensive experimental results validate the effectiveness of the fusion strategy and the loss design, outperforming state-of-the-art normal estimation schemes.

Connected through hyperedge HE-1 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data"):
  with members:
    - "large scale 6d object pose estimation dataset for industrial bin picking"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "photorealistic image synthesis for object instance detection"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "learning descriptors for object recognition and 3d pose estimation"

Connected through hyperedge HE-2 (induced by source paper "fuseseg lidar point cloud segmentation fusing multi modal data"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "cleargrasp 3d shape estimation of transparent objects for manipulation"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "the replica dataset a digital replica of indoor spaces"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "tom net learning transparent object matting from a single image"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "normal assisted stereo depth estimation"):
  with members:
    - "self supervised learning for single view depth and surface normal estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "dpsnet end to end deep plane sweep stereo"
    - "ga net guided aggregation net for end to end stereo matching"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-5 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "on single source robustness in deep fusion models"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "robust deep multi modal learning based on gated information fusion network"

Connected through hyperedge HE-6 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "grasp pose detection in point clouds"
    - "learning end to end multimodal sensor policies for autonomous navigation"

Connected through hyperedge HE-7 (induced by source paper "irs a large synthetic indoor robotics stereo dataset for disparity and surface normal estimation"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "group wise correlation stereo network"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast disparity estimation using dense networks"
    - "pyramid stereo matching network"
    - "semi global stereo matching with surface orientation priors"
    - "single shot object detection with enriched semantics"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-8 (induced by source paper "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"):
  with members:
    - "from rank estimation to rank approximation rank residual constraint for image denoising"
    - "group sparsity residual with non local samples for image denoising"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "fast resampling of three dimensional point clouds via graphs"
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

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)