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: "boundary cues for 3d object shape recovery"
  Abstract: Early work in computer vision considered a host of geometric cues for both shape reconstruction and recognition. However, since then, the vision community has focused heavily on shading cues for reconstruction, and moved towards data-driven approaches for recognition. In this paper, we reconsider these perhaps overlooked "boundary" cues (such as self occlusions and folds in a surface), as well as many other established constraints for shape reconstruction. In a variety of user studies and quantitative tasks, we evaluate how well these cues inform shape reconstruction (relative to each other) in terms of both shape quality and shape recognition. Our findings suggest many new directions for future research in shape reconstruction, such as automatic boundary cue detection and relaxing assumptions in shape from shading (e.g. orthographic projection, Lambertian surfaces).

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"
    - "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"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-2 (induced by source paper "silhouette guided point cloud reconstruction beyond occlusion"):
  with members:
    - "what do single view 3d reconstruction networks learn"
    - "dense 3d object reconstruction from a single depth view"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "soccer on your tabletop"
    - "pcn point completion network"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-3 (induced by source paper "simultaneous edge alignment and learning"):
  with members:
    - "iterative learning with open set noisy labels"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "annotating object instances with a polygon rnn"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"
    - "deep active contours"

Connected through hyperedge HE-4 (induced by source paper "casenet deep category aware semantic edge detection"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"
    - "weakly supervised object boundaries"
    - "pixel wise deep learning for contour detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"

Connected through hyperedge HE-5 (induced by source paper "inverse rendering techniques for physically grounded image editing"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "constructaide analyzing and visualizing construction sites through photographs and building models"
    - "blind recovery of spatially varying reflectance from a single image"
    - "depth transfer depth extraction from video using non parametric sampling"
    - "depth extraction from video using non parametric sampling"
    - "snaxels on a plane"
    - "user interface design for military ar applications"

Connected through hyperedge HE-6 (induced by source paper "lifting object detection datasets into 3d"):
  with members:
    - "microsoft coco common objects in context"

Connected through hyperedge HE-7 (induced by source paper "virtual view networks for object reconstruction"):
  with members:
    - "viewpoints and keypoints"
    - "simultaneous detection and segmentation"
    - "do convnets learn correspondence"
    - "depth transfer depth extraction from video using non parametric sampling"
    - "depth map prediction from a single image using a multi scale deep network"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "single view depth estimation from examples"

Connected through hyperedge HE-8 (induced by source paper "designing deep networks for surface normal estimation"):
  with members:
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "deeppose human pose estimation via deep neural networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)