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: "fast and accurate semantic mapping through geometric based incremental segmentation"
  Abstract: We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time. The proposed method assigns class probabilities to each region, not each element (e.g., surfel and voxel), of the 3D map which is built up through a robust SLAM framework and incrementally segmented with a geometric-based segmentation method. Differently from all other approaches, our method has a capability of running at over 30Hz while performing all processing components, including SLAM, segmentation, 2D recognition, and updating class probabilities of each segmentation label at every incoming frame, thanks to the high efficiency that characterizes the computationally intensive stages of our framework. By utilizing a specifically designed CNN to improve the frame-wise segmentation result, we can also achieve high accuracy. We validate our method on the NYUv2 dataset by comparing with the state of the art in terms of accuracy and computational efficiency, and by means of an analysis in terms of time and space complexity.

Connected through hyperedge HE-1 (induced by source paper "incremental class discovery for semantic segmentation with rgbd sensing"):
  with members:
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "segcloud semantic segmentation of 3d point clouds"
    - "pointwise convolutional neural networks"
    - "w net a deep model for fully unsupervised image segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "volumetric instance aware semantic mapping and 3d object discovery"):
  with members:
    - "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"
    - "incremental object database building 3d models from multiple partial observations"
    - "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"

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)