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 "bim assisted object recognition for the on site autonomous robotic assembly of discrete structures"):
  Abstract: Robots-operating autonomous assembly applications in an unstructured environment require precise methods to locate the#R##N#building components on site. However, the current available object detection systems are not well-optimised for construction#R##N#applications, due to the tedious setups incorporated for referencing an object to a system and inability to cope with the elements imperfections. In this paper, we propose a fexible object pose estimation framework to enable robots to autonomously#R##N#handle building components on-site with an error tolerance to build a specifc design target without the need to sort or label#R##N#them. We implemented an object recognition approach that uses the virtual representation model of all the objects found in#R##N#a BIM model to autonomously search for the best-matched objects in a scene. The design layout is used to guide the robot#R##N#to grasp and manipulate the found elements to build the desired structure. We verify our proposed framework by testing it#R##N#in an automatic discrete wall assembly workfow. Although the precision is not as expected, we analyse the possible reasons#R##N#that might cause this imprecision, which paves the path for future improvements.

Includes members:
  Member "segicp integrated deep semantic segmentation and pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "mobile robotic fabrication at 1 1 scale the in situ fabricator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "confusion sensor fusion for complex robotic systems using nonlinear optimization")

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)