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: "deepcontext context encoding neural pathways for 3d holistic scene understanding"
  Abstract: While deep neural networks have led to human-level performance on computer vision tasks, they have yet to demonstrate similar gains for holistic scene understanding. In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models. This paper presents an approach to embed 3D context into the topology of a neural network trained to perform holistic scene understanding. Given a depth image depicting a 3D scene, our network aligns the observed scene with a predefined 3D scene template, and then reasons about the existence and location of each object within the scene template. In doing so, our model recognizes multiple objects in a single forward pass of a 3D convolutional neural network, capturing both global scene and local object information simultaneously. To create training data for this 3D network, we generate partly hallucinated depth images which are rendered by replacing real objects with a repository of CAD models of the same object category. Extensive experiments demonstrate the effectiveness of our algorithm compared to the state-of-the-arts. Source code and data are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (induced by source paper "spherical kernel for efficient graph convolution on 3d point clouds"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "dense 3d face correspondence"
    - "learning to sample"

Connected through hyperedge HE-3 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-4 (induced by source paper "clouds of oriented gradients for 3d detection of objects surfaces and indoor scene layouts"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"
    - "3d object proposals using stereo imagery for accurate object class detection"

Connected through hyperedge HE-5 (induced by source paper "hierarchy denoising recursive autoencoders for 3d scene layout prediction"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "birdnet a 3d object detection framework from lidar information"
    - "complex yolo real time 3d object detection on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "octree guided cnn with spherical kernels for 3d point clouds"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-7 (induced by source paper "instance and category level 6d object pose estimation"):
  with members:
    - "category level 6d object pose recovery in depth images"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-8 (induced by source paper "deep context aware kernel networks"):
  with members:
    - "multi modal multi scale deep learning for large scale image annotation"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "scene parsing with global context embedding"
    - "sift meets cnn a decade survey of instance retrieval"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"

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)