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: "improving place recognition using dynamic object detection"
  Abstract: Traditional appearance-based place recognition algorithms based on handcrafted features have proven inadequate in environments with a significant presence of dynamic objects -- objects that may or may not be present in an agent's subsequent visits. Place representations from features extracted using Deep Learning approaches have gained popularity for their robustness and because the algorithms that used them yield better accuracy. Nevertheless, handcrafted features are still popular in devices that have limited resources. This article presents a novel approach that improves place recognition in environments populated by dynamic objects by incorporating the very knowledge of these objects to improve the overall quality of the representations of places used for matching. The proposed approach fuses object detection and place description, Deep Learning and handcrafted features, with the significance of reducing memory and storage requirements. This article demonstrates that the proposed approach yields improved place recognition accuracy, and was evaluated using both synthetic and real-world datasets. The adoption of the proposed approach will significantly improve place recognition results in environments populated by dynamic objects, and explored by devices with limited resources, with particular utility in both indoor and outdoor environments.

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.CV(Computer Vision and Pattern Recognition)