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: "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment"
  Abstract: Expansion of today’s underwater scenarios and missions necessitates the requisition for robust decision making of the autonomous underwater vehicle (AUV); hence, design an efficient decision-making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a global route planner (GRP) and a local path planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an uncertain underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large-scale environment. The LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles autonomy in terms of mission productivity, mission time management, and vehicle safety.

Connected through hyperedge HE-1 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations"):
  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"
    - "optimal route planning with prioritized task scheduling for auv missions"

Connected through hyperedge HE-2 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations"):
  with members:
    - "an efficient hybrid route path planning model for dynamic task allocation and safe maneuvering of an underwater vehicle in a realistic environment"
    - "differential evolution for efficient auv path planning in time variant uncertain underwater environment"
    - "a hierarchal planning framework for auv mission management in a spatio temporal varying ocean"
    - "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:
    - "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms"
    - "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:
    - "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms"
    - "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:
    - "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms"
    - "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:
    - "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms"
    - "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 "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms"):
  with members:
    - "real time quasi optimal trajectory planning for autonomous underwater docking"
    - "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"

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)