human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multimodal sensor based semantic 3d mapping for a large scale environment"):
  Abstract: Semantic 3D mapping is one of the most important fields in robotics, and has been used in many applications, such as robot navigation, surveillance, and virtual reality. In general, semantic 3D mapping is mainly composed of 3D reconstruction and semantic segmentation. As these technologies evolve, there has been great progress in semantic 3D mapping in recent years. Furthermore, the number of robotic applications requiring semantic information in 3D mapping to perform high-level tasks has increased, and many studies on semantic 3D mapping have been published. Existing methods use a camera for both 3D reconstruction and semantic segmentation. However, this is not suitable for large-scale environments and has the disadvantage of high computational complexity. To address this problem, we propose a multimodal sensor-based semantic 3D mapping system using a 3D Lidar combined with a camera. In this study, we build a 3D map by estimating odometry based on a global positioning system (GPS) and an inertial measurement unit (IMU), and use the latest 2D convolutional neural network (CNN) for semantic segmentation. To build a semantic 3D map, we integrate the 3D map with semantic information by using coordinate transformation and Bayes' update scheme. In order to improve the semantic 3D map, we propose a 3D refinement process to correct wrongly segmented voxels and remove traces of moving vehicles in the 3D map. Through experiments on challenging sequences, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and intersection over union (IoU). Thus, our method can be used for various applications that require semantic information in 3D map.

Includes members:
  Member "semantic 3d occupancy mapping through efficient high order crfs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "orb slam2 an open source slam system for monocular stereo and rgb d cameras":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "meaningful maps with object oriented semantic mapping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "semanticfusion dense 3d semantic mapping with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "semi dense 3d semantic mapping from monocular slam":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "efficient inference in fully connected crfs with gaussian edge potentials":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")

Question: Please tell me which class the source paper 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)