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: "joint prediction of depths normals and surface curvature from rgb images using cnns"
  Abstract: Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image. To the best of our knowledge this is the first work to estimate surface curvature from colour using a machine learning approach. Additionally, we demonstrate that by tuning the network to infer well designed features, such as surface curvature, we can achieve improved performance at estimating depth and normals.This indicates that network guidance is still a useful aspect of designing and training a neural network. We run extensive experiments where the network is trained to infer different tasks while the model capacity is kept constant resulting in different feature maps based on the tasks at hand. We outperform the previous state-of-the-art benchmarks which jointly estimate depths and surface normals while predicting surface curvature in parallel.

Connected through hyperedge HE-1 (induced by source paper "360 o surface regression with a hyper sphere loss"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "three for one and one for three flow segmentation and surface normals"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-2 (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"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning for single view depth and surface normal estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "lego learning edge with geometry all at once by watching videos"
    - "unsupervised learning of depth and ego motion from video"
    - "unsupervised learning of geometry with edge aware depth normal consistency"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-4 (induced by source paper "pano popups indoor 3d reconstruction with a plane aware network"):
  with members:
    - "convolutions on spherical images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "megadepth learning single view depth prediction from internet photos"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "learning spherical convolution for fast features from 360 deg imagery"

Connected through hyperedge HE-5 (induced by source paper "representation similarity analysis for efficient task taxonomy transfer learning"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "blitznet a real time deep network for scene understanding"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "floors are flat leveraging semantics for real time surface normal prediction"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "unsupervised learning of geometry with edge aware depth normal consistency"
    - "cross domain self supervised multi task feature learning using synthetic imagery"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "marr revisited 2d 3d alignment via surface normal prediction"

Connected through hyperedge HE-7 (induced by source paper "real time joint semantic segmentation and depth estimation using asymmetric annotations"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "blitznet a real time deep network for scene understanding"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "icnet for real time semantic segmentation on high resolution images"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "eng end to end neural geometry for robust depth and pose estimation using cnns"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "probflow joint optical flow and uncertainty estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "demon depth and motion network for learning monocular stereo"
    - "semi supervised deep learning for monocular depth map prediction"

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)