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: "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms"
  Abstract: In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a nonlinear optimal control problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time.Four evolutionary based path planning methods namely particle swarm optimization (PSO), biogeography-based optimization (BBO), differential evolution (DE) and Firefly algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analysis the performance and capability of the mentioned methods for guiding an AUV from loitering point toward the rendezvous place through a comprehensive simulation study.The proposed planner module entails a heuristic for refining the path considering situational awareness of underlying environment, encompassing static and dynamic obstacles overwhelmed in spatiotemporal current vectors.This leads to accommodate the unforeseen changes in the operating field like emergence of unpredicted obstacles or variability of current vector filed and turbulent regions. The simulation results demonstrate the inherent robustness and significant efficiency of the proposed planner in enhancement of the vehicle's autonomy in terms of using current force, coping undesired current disturbance for the desired rendezvous purpose. Advantages and shortcoming of all utilized methods are also presented based on the obtained results.

Connected through hyperedge HE-1 (induced by source paper "person following by autonomous robots a categorical overview"):
  with members:
    - "visual diver recognition for underwater human robot collaboration"
    - "robot communication via motion closing the underwater human robot interaction loop"
    - "caddy underwater stereo vision dataset for human robot interaction hri in the context of diver activities"
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "underwater multi robot convoying using visual tracking by detection"
    - "real time 3d human tracking for mobile robots with multisensors"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

Connected through hyperedge HE-2 (induced by source paper "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment"):
  with members:
    - "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment"
    - "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"
    - "toward efficient task assignment and motion planning for large scale underwater mission"
    - "optimal route planning with prioritized task scheduling for auv missions"

Connected through hyperedge HE-3 (induced by source paper "development of an autonomous reactive mission scheduling and path planning armsp architecture using evolutionary algorithms for auv operation in a sever ocean environment"):
  with members:
    - "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment"
    - "an autonomous reactive architecture for efficient auv mission time management in realistic severe ocean environment"
    - "an efficient hybrid route path planning model for dynamic task allocation and safe maneuvering of an underwater vehicle in a realistic environment"
    - "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment"
    - "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"
    - "toward efficient task assignment and motion planning for large scale underwater mission"
    - "optimal route planning with prioritized task scheduling for auv missions"

Connected through hyperedge HE-4 (induced by source paper "an autonomous reactive architecture for efficient auv mission time management in realistic severe ocean environment"):
  with members:
    - "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment"
    - "an efficient hybrid route path planning model for dynamic task allocation and safe maneuvering of an underwater vehicle in a realistic environment"
    - "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment"
    - "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"
    - "toward efficient task assignment and motion planning for large scale underwater mission"
    - "optimal route planning with prioritized task scheduling for auv missions"

Connected through hyperedge HE-5 (induced by source paper "a hierarchal planning framework for auv mission management in a spatio temporal varying ocean"):
  with members:
    - "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment"
    - "an efficient hybrid route path planning model for dynamic task allocation and safe maneuvering of an underwater vehicle in a realistic environment"
    - "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment"
    - "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"
    - "toward efficient task assignment and motion planning for large scale underwater mission"
    - "optimal route planning with prioritized task scheduling for auv missions"

Connected through hyperedge HE-6 (induced by source paper "an efficient hybrid route path planning model for dynamic task allocation and safe maneuvering of an underwater vehicle in a realistic environment"):
  with members:
    - "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment"
    - "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment"
    - "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"
    - "toward efficient task assignment and motion planning for large scale underwater mission"
    - "optimal route planning with prioritized task scheduling for auv missions"

Connected through hyperedge HE-7 (induced by source paper "toward efficient task assignment and motion planning for large scale underwater mission"):
  with members:
    - "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment"
    - "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"

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)