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: "an open source fiducial based visual inertial state estimation system"
  Abstract: Many robotic tasks rely on the estimation of the location of moving bodies with respect to the robotic workspace. This information about the robots pose and velocities is usually either directly used for localization and control or utilized for verification. Often motion capture systems are used to obtain such a state estimation. However, these systems are very costly and limited in terms of workspace size and outdoor usage. Therefore, we propose a lightweight and easy to use, visual inertial Simultaneous Localization and Mapping approach that leverages paper printable artificial landmarks, so called fiducials. Results show that by fusing visual and inertial data, the system provides accurate estimates and is robust against fast motions. Continuous estimation of the fiducials within the workspace ensures accuracy and avoids additional calibration. By providing an open source implementation and various datasets including ground truth information, we enable other community members to run, test, modify and extend the system using datasets or their own robotic setups.

Connected through hyperedge HE-1 (induced by source paper "ucoslam simultaneous localization and mapping by fusion of keypoints and squared planar markers"):
  with members:
    - "ldso direct sparse odometry with loop closure"
    - "artificial intelligence for long term robot autonomy a survey"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pop up slam semantic monocular plane slam for low texture environments"
    - "mapping and localization from planar markers"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "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)