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: "a brain inspired compact cognitive mapping system"
  Abstract: As the robot explores the environment, the map grows over time in the simultaneous localization and mapping (SLAM) system, especially for the large scale environment. The ever-growing map prevents long-term mapping. In this paper, we developed a compact cognitive mapping approach inspired by neurobiological experiments. Inspired from neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are proposed to describe one of distinct segments of the explored environment. The vertices and edges with movement information below the threshold of the neighborhood fields are avoided adding to the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem, which can be efficiently solved by the fast open linear solvers as a general problem. According to the cognitive decision-making of familiar environments, loop closure edges are clustered depending on time intervals, and then parallel computing is applied to perform batch global optimization of the cognitive map for ensuring the efficiency of computation and real-time performance. After the loop closure process, scene integration is performed, in which revisited vertices are removed subsequently to further reduce the size of the cognitive map. A monocular visual SLAM system is developed to test our approach in a rat-like maze environment. Our results suggest that the method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layer of the environment as the standard one. Experiments demonstrate that our method is very suited for compact cognitive mapping to support long-term robot mapping. Our approach is simple, but pragmatic and efficient for achieving the compact cognitive map.

Connected through hyperedge HE-1 (induced by source paper "stereoneurobayesslam a neurobiologically inspired stereo visual slam system based on direct sparse method"):
  with members:
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

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)