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: "safe sequential path planning of multi vehicle systems under presence of disturbances and imperfect information"
  Abstract: Recently, there has been an immense surge of interest in using unmanned aerial vehicles (UAVs) for civil purposes. Multi-UAV systems are safety-critical, and safety guarantees must be made to ensure no undesirable configurations such as collisions occur. Hamilton-Jacobi (HJ) reachability is ideal for analyzing such safety-critical systems because it provides safety guarantees and is flexible in terms of system dynamics; however, its direct application is limited to small-scale systems of no more than two vehicles because of the exponential-scaling computation complexity. By assigning vehicle priorities, the sequential path planning (SPP) method allows multi-vehicle path planning to be done with a computation complexity that scales linearly with the number of vehicles. Previously the SPP method assumed no disturbances in the vehicle dynamics, and that every vehicle has perfect knowledge of the position of higher-priority vehicles. In this paper, we make SPP more practical by providing three different methods for accounting for disturbances in dynamics and imperfect knowledge of higher-priority vehicles. Each method has advantages and disadvantages with different assumptions about information sharing. We demonstrate our proposed methods in simulations.

Connected through hyperedge HE-1 (induced by source paper "a scalable framework for real time multi robot multi human collision avoidance"):
  with members:
    - "classification based approximate reachability with guarantees applied to safe trajectory tracking"
    - "a classification based approach for approximate reachability"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "robust tracking with model mismatch for fast and safe planning an sos optimization approach"
    - "cooperative multi agent planning a survey"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"

Connected through hyperedge HE-2 (induced by source paper "hamilton jacobi reachability a brief overview and recent advances"):
  with members:
    - "recursive regression with neural networks approximating the hji pde solution"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"
    - "funnel libraries for real time robust feedback motion planning"
    - "using neural networks to compute approximate and guaranteed feasible hamilton jacobi bellman pde solutions"
    - "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming"

Connected through hyperedge HE-3 (induced by source paper "safe and resilient multi vehicle trajectory planning under adversarial intruder"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming"
    - "safe sequential path planning of multi vehicle systems via double obstacle hamilton jacobi isaacs variational inequality"

Connected through hyperedge HE-4 (induced by source paper "fastrack a modular framework for fast and guaranteed safe motion planning"):
  with members:
    - "funnel libraries for real time robust feedback motion planning"
    - "vision and learning for deliberative monocular cluttered flight"

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.MA(Multiagent Systems)