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: "planematch patch coplanarity prediction for robust rgb d reconstruction"
  Abstract: We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural net that takes in RGB, depth, and normal information of a planar patch in an image and outputs a descriptor that can be used to find coplanar patches from other images.We train the network on 10 million triplets of coplanar and non-coplanar patches, and evaluate on a new coplanarity benchmark created from commodity RGB-D scans. Experiments show that our learned descriptor outperforms alternatives extended for this new task by a significant margin. In addition, we demonstrate the benefits of coplanarity matching in a robust RGBD reconstruction formulation.We find that coplanarity constraints detected with our method are sufficient to get reconstruction results comparable to state-of-the-art frameworks on most scenes, but outperform other methods on standard benchmarks when combined with a simple keypoint method.

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"
    - "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"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (induced by source paper "rgb d odometry and slam"):
  with members:
    - "cam convs camera aware multi scale convolutions for single view depth"
    - "direct sparse mapping"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "rgbid slam for accurate real time localisation and 3d mapping"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "a micro lie theory for state estimation in robotics"

Connected through hyperedge HE-3 (induced by source paper "floorplan priors for joint camera pose and room layout estimation"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "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)