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: "incremental sampling based algorithm for minimum violation motion planning"
  Abstract: This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal while satisfying a set of safety rules. Particular attention is devoted to goals that become feasible only if a subset of the safety rules are violated. The proposed algorithm computes a control law, that minimizes the level of unsafety while the desired goal is guaranteed to be reached. This problem is motivated by an autonomous car navigating an urban environment while following rules of the road such as "always travel in right lane'' and "do not change lanes frequently''. Ideas behind sampling based motion-planning algorithms, such as Probabilistic Road Maps (PRMs) and Rapidly-exploring Random Trees (RRTs), are employed to incrementally construct a finite concretization of the dynamics as a durational Kripke structure. In conjunction with this, a weighted finite automaton that captures the safety rules is used in order to find an optimal trajectory that minimizes the violation of safety rules. We prove that the proposed algorithm guarantees asymptotic optimality, i.e., almost-sure convergence to optimal solutions. We present results of simulation experiments and an implementation on an autonomous urban mobility-on-demand system.

Connected through hyperedge HE-1 (induced by source paper "revisiting the asymptotic optimality of rrt"):
  with members:
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "the critical radius in sampling based motion planning"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "kinodynamic rrt optimal motion planning for systems with linear differential constraints"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "liability ethics and culture aware behavior specification using rulebooks"):
  with members:
    - "on a formal model of safe and scalable self driving cars"
    - "negotiating the probabilistic satisfaction of temporal logic motion specifications"
    - "minimum violation ltl planning with conflicting specifications"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-3 (induced by source paper "integer programming as a general solution methodology for path based optimization in robotics principles best practices and applications"):
  with members:
    - "multi agent path finding with deadlines"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "optimal tourist problem and anytime planning of trip itineraries"
    - "k color multi robot motion planning"

Connected through hyperedge HE-4 (induced by source paper "on maximizing lateral clearance of an autonomous vehicle in urban environments"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "pampc perception aware model predictive control for quadrotors"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "a lexicographic search method for multi objective motion planning"):
  with members:
    - "a bi criteria path planning algorithm for robotics applications"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "from specifications to behavior maneuver verification in a semantic state space"):
  with members:
    - "partitioning of the free space time for on road navigation of autonomous ground vehicles"

Connected through hyperedge HE-7 (induced by source paper "interpretable apprenticeship learning with temporal logic specifications"):
  with members:
    - "from ltl to deterministic automata a safraless compositional approach"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-8 (induced by source paper "time window temporal logic"):
  with members:
    - "on the minimal revision problem of specification automata"
    - "transition complexity of incomplete dfas"

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)