human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an autonomous reactive architecture for efficient auv mission time management in realistic severe ocean environment"):
  Abstract: Today AUVs operation still remains restricted to very particular tasks with low real autonomy due to battery restrictions. Efficient motion planning and mission scheduling are principle requirement toward advance autonomy and facilitate the vehicle to handle long-range operations. A single vehicle cannot carry out all tasks in a large scale terrain; hence, it needs a certain degree of autonomy in performing robust decision making and awareness of the mission/environment to trade-off between tasks to be completed, managing the available time, and ensuring safe deployment at all stages of the mission. In this respect, this research introduces a modular control architecture including higher/lower level planners, in which the higher level module is responsible for increasing mission productivity by assigning prioritized tasks while guiding the vehicle toward its final destination in a terrain covered by several waypoints; and the lower level is responsible for vehicle's safe deployment in a smaller scale encountering time-varying ocean current and different uncertain static/moving obstacles similar to actual ocean environment. Synchronization between higher and lower level modules is efficiently configured to manage the mission time and to guarantee on-time termination of the mission. The performance and accuracy of two higher and lower level modules are tested and validated using ant colony and firefly optimization algorithm, respectively. After all, the overall performance of the architecture is investigated in 10 different mission scenarios. The analyze of the captured results from different simulated missions confirm the efficiency and inherent robustness of the introduced architecture in efficient time management, safe deployment, and providing beneficial operation by proper prioritizing the tasks in accordance with mission time.

Includes members:
  Member "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations")
  Member "auv rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "person following by autonomous robots a categorical overview")
  Member "an efficient hybrid route path planning model for dynamic task allocation and safe maneuvering of an underwater vehicle in a realistic environment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations")
  Member "a novel efficient task assign route planning method for auv guidance in a dynamic cluttered environment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations")
  Member "biogeography based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task time management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations")
  Member "differential evolution for efficient auv path planning in time variant uncertain underwater environment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment")
  Member "toward efficient task assignment and motion planning for large scale underwater mission":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel versatile architecture for autonomous underwater vehicle s motion planning and task assignment")
  Member "optimal route planning with prioritized task scheduling for auv missions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient deployment and mission timing of autonomous underwater vehicles in large scale operations")

Question: Please tell me which class the source paper 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)