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: "learning rich features from rgb d images for object detection and segmentation"
  Abstract: In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an average precision of 37.3%, which is a 56% relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to object instances found by our detector. For this task, we propose a decision forest approach that classifies pixels in the detection window as foreground or background using a family of unary and binary tests that query shape and geocentric pose features. Finally, we use the output from our object detectors in an existing superpixel classification framework for semantic scene segmentation and achieve a 24% relative improvement over current state-of-the-art for the object categories that we study. We believe advances such as those represented in this paper will facilitate the use of perception in fields like robotics.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "deep learning for 3d point clouds a survey"
    - "bsp net generating compact meshes via binary space partitioning"
    - "pixel2mesh multi view 3d mesh generation via deformation"

Connected through hyperedge HE-2 (induced by source paper "mti net multi scale task interaction networks for multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "deep high resolution representation learning for visual recognition"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic feature pyramid networks"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-3 (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-4 (induced by source paper "rgb based semantic segmentation using self supervised depth pre training"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "cross stitch networks for multi task learning"
    - "identity mappings in deep residual networks"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-5 (induced by source paper "3d gated recurrent fusion for semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "adaptive fusion for rgb d salient object detection"
    - "efficient semantic scene completion network with spatial group convolution"
    - "scalable place recognition under appearance change for autonomous driving"
    - "two stream 3d semantic scene completion"
    - "unsupervised rgbd video object segmentation using gans"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-6 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "shallow2deep indoor scene modeling by single image understanding"):
  with members:
    - "learning shape priors for single view 3d completion and reconstruction"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "a simple neural network module for relational reasoning"
    - "roomnet end to end room layout estimation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "a simple pooling based design for real time salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "pdnet prior model guided depth enhanced network for salient object detection"
    - "rgbd salient object detection via deep fusion"
    - "non local neural networks"
    - "attention is all you need"

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)