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: "svin2 sonar visual inertial slam with loop closure for underwater navigation"
  Abstract: This paper presents a novel tightly-coupled keyframe based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted to the underwater domain. The state-of-the-art visual-inertial state estimation package OKVIS has been significantly augmented to accommodate acoustic data from sonar and depth measurements from pressure sensor, along with visual and inertial data in a non-linear optimization-based framework. The main contributions of this paper are: a robust initialization method to refine scale using depth measurements and a real-time loop-closing and relocalization method. An additional contribution is the tightly-coupled optimization formulation using acoustic, visual, inertial, and depth data. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate the performance of our approach.

Connected through hyperedge HE-1 (induced by source paper "experimental comparison of open source visual inertial based state estimation algorithms in the underwater domain"):
  with members:
    - "an underwater slam system using sonar visual inertial and depth sensor"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "visual inertial monocular slam with map reuse"
    - "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)