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 motion capture system"
  Abstract: Many robotic tasks rely on the accurate localization of moving objects within a given workspace. This information about the objects' poses and velocities are used for control,motion planning, navigation, interaction with the environment or verification. Often motion capture systems are used to obtain such a state estimate. However, these systems are often costly, limited in workspace size and not suitable for outdoor usage. Therefore, we propose a lightweight and easy to use, visual-inertial Simultaneous Localization and Mapping approach that leverages cost-efficient, paper printable artificial landmarks, socalled fiducials. Results show that by fusing visual and inertial data, the system provides accurate estimates and is robust against fast motions and changing lighting conditions. Tight integration of the estimation of sensor and fiducial pose as well as extrinsics ensures accuracy, map consistency and avoids the requirement for precalibration. By providing an open source implementation and various datasets, partially with ground truth information, we enable community members to run, test, modify and extend the system either using these datasets or directly running the system on their own robotic setups.

Connected through hyperedge HE-1 (induced by source paper "confusion sensor fusion for complex robotic systems using nonlinear optimization"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "on manifold preintegration for real time visual inertial odometry"
    - "mobile robotic fabrication at 1 1 scale the in situ fabricator"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-2 (induced by source paper "tagslam robust slam with fiducial markers"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-3 (induced by source paper "resolving marker pose ambiguity by robust rotation averaging with clique constraints"):
  with members:
    - "mapping and localization from planar markers"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-4 (induced by source paper "dynamic markers uav landing proof of concept"):
  with members:
    - "moma visual mobile marker odometry"
    - "mapping and localization from planar markers"
    - "mutual localization two camera relative 6 dof pose estimation from reciprocal fiducial observation"

Connected through hyperedge HE-5 (induced by source paper "efficient kinematic planning for mobile manipulators with non holonomic constraints using optimal control"):
  with members:
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "mapping and localization from planar markers"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"
    - "robust camera location estimation by convex programming"

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)