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 "drrt scalable and informed asymptotically optimal multi robot motion planning"):
  Abstract: Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-quality paths in such scenarios can be achieved, in principle, by exploring the composite space of all robots. Sampling-based planners do so by building a roadmap or a tree data structure in the corresponding configuration space and can achieve asymptotic optimality. The hardness of motion planning, however, renders the explicit construction of such structures in the composite space of multiple robots impractical. This work proposes a scalable solution for such coupled multi-robot problems, which provides desirable path-quality guarantees and is also computationally efficient. In particular, the proposed $$\mathtt{dRRT^*}$$ is an informed, asymptotically-optimal extension of a prior sampling-based multi-robot motion planner, $$\mathtt{dRRT}$$. The prior approach introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space. This work identifies the conditions for convergence to optimal paths in multi-robot problems, which the prior method was not achieving. Building on this analysis, $$\mathtt{dRRT}$$ is first properly adapted so as to achieve the theoretical guarantees and then further extended so as to make use of effective heuristics when searching the composite space of all robots. The case where the various robots share some degrees of freedom is also studied. Evaluation in simulation indicates that the new algorithm, $$\mathtt{dRRT^*}$$  converges to high-quality paths quickly and scales to a higher number of robots where various alternatives fail. This work also demonstrates the planner’s capability to solve problems involving multiple real-world robotic arms.

Includes members:
  Member "scalable asymptotically optimal multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anytime multi arm task and motion planning for pick and place of individual objects via handoffs")
  Member "effective metrics for multi robot motion planning":
    also connected through other hyperedges:
      (none)
  Member "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  Member "motion planning for unlabeled discs with optimality guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient multi agent motion planning in continuous workspaces using medial axis based swap graphs")
  Member "on the hardness of unlabeled multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on rearrangement of items stored in stacks")
  Member "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "efficient multi robot motion planning for unlabeled discs in simple polygons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity of planning")
  Member "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visualizing local minima in multi robot motion planning using morse theory")

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)