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: "indoor scene understanding in 2 5 3d for autonomous agents a survey"
  Abstract: This work was supported by the Australian Research Council’s Discovery Projects Funding Scheme under Project DP150104645.

Connected through hyperedge HE-1 (induced by source paper "lightweight residual densely connected convolutional neural network"):
  with members:
    - "denser deep evolutionary network structured representation"
    - "residual dense network for image restoration"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "residual dense network for image super resolution"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "resolving 3d human pose ambiguities with 3d scene constraints"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "estimating 3d motion and forces of person object interactions from monocular video"
    - "learning joint reconstruction of hands and manipulated objects"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"
    - "open3d a modern library for 3d data processing"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-3 (induced by source paper "unsupervised primitive discovery for improved 3d generative modeling"):
  with members:
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "visual affordance and function understanding a survey"
    - "so net self organizing network for point cloud analysis"
    - "volumetric convolution automatic representation learning in unit ball"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "active scene understanding via online semantic reconstruction"):
  with members:
    - "object aware guidance for autonomous scene reconstruction"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "robot in a room toward perfect object recognition in closed environments"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-5 (induced by source paper "3d point capsule networks"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "generalized capsule networks with trainable routing procedure"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"

Connected through hyperedge HE-6 (induced by source paper "floorplan priors for joint camera pose and room layout estimation"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

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)