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: "low level active visual navigation increasing robustness of vision based localization using potential fields"
  Abstract: This paper proposes a low-level visual navigation algorithm to improve visual localization of a mobile robot. The algorithm, based on artificial potential fields, associates each feature in the current image frame with an attractive or neutral potential energy, with the objective of generating a control action that drives the vehicle towards the goal, while still favoring feature-rich areas within a local scope, \replaced{thus improving}{improving in this way} the localization performance. One key property of the proposed method is that it does not rely on mapping, and therefore it is a lightweight solution that can be deployed on miniaturized aerial robots, in which memory and computational power are major constraints. Simulations and real experimental results using a mini quadrotor equipped with a downward looking camera demonstrate that the proposed method can effectively drive the vehicle to \replaced{a designated}{the} goal through a path that prevents localization failure.

Connected through hyperedge HE-1 (induced by source paper "active depth estimation stability analysis and its applications"):
  with members:
    - "active estimation of 3d lines in spherical coordinates"
    - "a framework for depth estimation and relative localization of ground robots using computer vision"
    - "active structure from motion for 3d straight lines"

Connected through hyperedge HE-2 (induced by source paper "perception aware autonomous mast motion planning for planetary exploration rovers"):
  with members:
    - "bi directional value learning for risk aware planning under uncertainty"
    - "despot online pomdp planning with regularization"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "efficient planning under uncertainty with macro actions"
    - "sampling based algorithms for optimal motion planning"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-3 (induced by source paper "a framework for depth estimation and relative localization of ground robots using computer vision"):
  with members:
    - "active structure from motion for 3d straight lines"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "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)