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 algorithms for optimal motion planning"
  Abstract: During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs) have been shown to work well in practice and to possess theoretical guarantees such as probabilistic completeness. However, no theoretical bounds on the quality of the solution obtained by these algorithms have been established so far. The first contribution of this paper is a negative result: it is proven that, under mild technical conditions, the cost of the best path in the RRT converges almost surely to a non-optimal value. Second, a new algorithm is considered, called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost of the best path in the RRG converges to the optimum almost surely. Third, a tree version of RRG is introduced, called the RRT$^*$ algorithm, which preserves the asymptotic optimality of RRG while maintaining a tree structure like RRT. The analysis of the new algorithms hinges on novel connections between sampling-based motion planning algorithms and the theory of random geometric graphs. In terms of computational complexity, it is shown that the number of simple operations required by both the RRG and RRT$^*$ algorithms is asymptotically within a constant factor of that required by RRT.

Connected through hyperedge HE-1 (induced by source paper "bayesian local sampling based planning"):
  with members:
    - "balancing global exploration and local connectivity exploitation with rapidly exploring random disjointed trees"
    - "learning sampling distributions for robot motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "probabilistic approach to physical object disentangling"):
  with members:
    - "classifying and sorting cluttered piles of unknown objects with robots a learning approach"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-3 (induced by source paper "rationally inattentive path planning via rrt"):
  with members:
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "an efficient b spline based kinodynamic replanning framework for quadrotors"):
  with members:
    - "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"
    - "trajectory replanning for quadrotors using kinodynamic search and elastic optimization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "search based motion planning for aggressive flight in se 3"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-5 (induced by source paper "3d multi robot patrolling with a two level coordination strategy"):
  with members:
    - "rcamp a resilient communication aware motion planner for mobile robots with autonomous repair of wireless connectivity"
    - "segmatch segment based place recognition in 3d point clouds"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "multi robot adversarial patrolling facing a full knowledge opponent"

Connected through hyperedge HE-6 (induced by source paper "navigation in the presence of obstacles for an agile autonomous underwater vehicle"):
  with members:
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"

Connected through hyperedge HE-7 (induced by source paper "local sampling based planning with sequential bayesian updates"):
  with members:
    - "balancing global exploration and local connectivity exploitation with rapidly exploring random disjointed trees"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "online motion planning over multiple homotopy classes with gaussian process inference"):
  with members:
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

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)