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: "enforcing geometric constraints of virtual normal for depth prediction"
  Abstract: Monocular depth prediction plays a crucial role in understanding 3D scene geometry. Although recent methods have achieved impressive progress in evaluation metrics such as the pixel-wise relative error, most methods neglect the geometric constraints in the 3D space. In this work, we show the importance of the high-order 3D geometric constraints for depth prediction. By designing a loss term that enforces one simple type of geometric constraints, namely, virtual normal directions determined by randomly sampled three points in the reconstructed 3D space, we can considerably improve the depth prediction accuracy. Significantly, the byproduct of this predicted depth being sufficiently accurate is that we are now able to recover good 3D structures of the scene such as the point cloud and surface normal directly from the depth, eliminating the necessity of training new sub-models as was previously done. Experiments on two benchmarks: NYU Depth-V2 and KITTI demonstrate the effectiveness of our method and state-of-the-art performance.

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

Connected through hyperedge HE-2 (induced by source paper "diversedepth affine invariant depth prediction using diverse data"):
  with members:
    - "a large rgb d dataset for semi supervised monocular depth estimation"
    - "on the power of curriculum learning in training deep networks"
    - "learning the depths of moving people by watching frozen people"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "how do neural networks see depth in single images"
    - "learning depth with convolutional spatial propagation network"
    - "deep ordinal regression network for monocular depth estimation"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-3 (induced by source paper "structured knowledge distillation for dense prediction"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "attacking optical flow"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"

Connected through hyperedge HE-5 (induced by source paper "exploiting temporal consistency for real time video depth estimation"):
  with members:
    - "rethinking monocular depth estimation with adversarial training"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepvs a deep learning based video saliency prediction approach"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "from big to small multi scale local planar guidance for monocular depth estimation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-7 (induced by source paper "fcos fully convolutional one stage object detection"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "feature selective anchor free module for single shot object detection"
    - "deep high resolution representation learning for human pose estimation"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "an end to end textspotter with explicit alignment and attention"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-8 (induced by source paper "task aware monocular depth estimation for 3d object detection"):
  with members:
    - "towards instance level image to image translation"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "deep attention based classification network for robust depth prediction"
    - "deep ordinal regression network for monocular depth estimation"

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)