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: "incremental object database building 3d models from multiple partial observations"
  Abstract: Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.

Connected through hyperedge HE-1 (induced by source paper "deep slam object level rgbd slam based on class specific deep shape priors"):
  with members:
    - "dense object reconstruction from rgbd images with embedded deep shape representations"
    - "fusion volumetric object level slam"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "octnetfusion learning depth fusion from data"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "semantic photometric bundle adjustment on natural sequences"

Connected through hyperedge HE-2 (induced by source paper "volumetric instance aware semantic mapping and 3d object discovery"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "fusion volumetric object level slam"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "learning to segment every thing"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-3 (induced by source paper "versavis an open versatile multi camera visual inertial sensor suite"):
  with members:
    - "experimental comparison of visual aided odometry methods for rail vehicles"
    - "the double sphere camera model"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "pirvs an advanced visual inertial slam system with flexible sensor fusion and hardware co design"

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.RO(Robotics)