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: "motion planning strategies for autonomously mapping 3d structures"
  Abstract: This paper presents a system capable of autonomously mapping the visible part of a bounded three-dimensional structure using a mobile ground robot equipped with a depth sensor. We describe motion planning strategies to determine appropriate successive viewpoints and attempt to fill holes automatically in a point cloud produced by the sensing and perception layer. We develop a local motion planner using potential fields to maintain a desired distance from the structure. The emphasis is on accurately reconstructing a 3D model of a structure of moderate size rather than mapping large open environments, with applications for example in architecture, construction and inspection. The proposed algorithms do not require any initialization in the form of a mesh model or a bounding box. We compare via simulations the performance of our policies to the classic frontier based exploration algorithm. We illustrate the efficacy of our approach for different structure sizes, levels of localization accuracy and range of the depth sensor.

Connected through hyperedge HE-1 (induced by source paper "object aware guidance for autonomous scene reconstruction"):
  with members:
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "3dmatch learning local geometric descriptors from rgb d reconstructions"
    - "3dmatch learning the matching of local 3d geometry in range scans"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"

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)