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 via double obstacle hamilton jacobi isaacs variational inequality"
  Abstract: We consider the problem of planning trajectories for a group of $N$ vehicles, each aiming to reach its own target set while avoiding danger zones of other vehicles. The analysis of problems like this is extremely important practically, especially given the growing interest in utilizing unmanned aircraft systems for civil purposes. The direct solution of this problem by solving a single-obstacle Hamilton-Jacobi-Isaacs (HJI) variational inequality (VI) is numerically intractable due to the exponential scaling of computation complexity with problem dimensionality. Furthermore, the single-obstacle HJI VI cannot directly handle situations in which vehicles do not have a common scheduled arrival time. Instead, we perform sequential path planning by considering vehicles in order of priority, modeling higher-priority vehicles as time-varying obstacles for lower-priority vehicles. To do this, we solve a double-obstacle HJI VI which allows us to obtain the reach-avoid set, defined as the set of states from which a vehicle can reach its target while staying within a time-varying state constraint set. From the solution of the double-obstacle HJI VI, we can also extract the latest start time and the optimal control for each vehicle. This is a first application of the double-obstacle HJI VI which can handle systems with time-varying dynamics, target sets, and state constraint sets, and results in computation complexity that scales linearly, as opposed to exponentially, with the number of vehicles in consideration.

Connected through hyperedge HE-1 (induced by source paper "inference based strategy alignment for general sum differential games"):
  with members:
    - "on gradient based learning in continuous games"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "on the convergence of gradient based learning in continuous games"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"

Connected through hyperedge HE-2 (induced by source paper "a hamilton jacobi formulation for optimal coordination of heterogeneous multiple vehicle systems"):
  with members:
    - "a level set approach to online sensing and trajectory optimization with time delays"

Connected through hyperedge HE-3 (induced by source paper "ttr based rewards for reinforcement learning with implicit model priors"):
  with members:
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "proximal policy optimization algorithms"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming"

Connected through hyperedge HE-4 (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-5 (induced by source paper "modeling supervisor safe sets for improving collaboration in human robot teams"):
  with members:
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-6 (induced by source paper "a general safety framework for learning based control in uncertain robotic systems"):
  with members:
    - "adversarial attacks on neural network policies"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "transfer from simulation to real world through learning deep inverse dynamics model"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "a modified riccati transformation for decentralized computation of the viability kernel under lti dynamics"
    - "safe exploration in markov decision processes"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-7 (induced by source paper "scalable synthesis of safety certificates from data with application to learning based control"):
  with members:
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "scalable safety preserving robust control synthesis for continuous time linear systems"

Connected through hyperedge HE-8 (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 under presence of disturbances and imperfect information"

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)