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: "exploiting 2d floorplan for building scale panorama rgbd alignment"
  Abstract: This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data are shared with the community to further enhance indoor mapping research.

Connected through hyperedge HE-1 (induced by source paper "is this the right place geometric semantic pose verification for indoor visual localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "taskonomy disentangling task transfer learning"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "vlase vehicle localization by aggregating semantic edges"
    - "long term visual localization using semantically segmented images"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-2 (induced by source paper "floorplan jigsaw jointly estimating scene layout and aligning partial scans"):
  with members:
    - "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"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"
    - "fine to coarse global registration of rgb d scans"

Connected through hyperedge HE-3 (induced by source paper "inloc indoor visual localization with dense matching and view synthesis"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "planet photo geolocation with convolutional neural networks"
    - "image based localization using lstms for structured feature correlation"
    - "fine to coarse global registration of rgb d scans"
    - "dsac differentiable ransac for camera localization"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-4 (induced by source paper "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"):
  with members:
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "data efficient decentralized visual slam"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"
    - "a framework for the volumetric integration of depth images"

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)