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 "motion planning for multi link robots by implicit configuration space tiling"):
  Abstract: We study the problem of motion-planning for free-flying multi-link robots and develop a sampling-based algorithm that is specifically tailored for the task. Our work is based on the simple observation that the set of configurations for which the robot is self-collision free is independent of the obstacles or of the exact placement of the robot. This allows to eliminate the need to perform costly self-collision checks online when solving motion-planning problems, assuming some offline preprocessing. In particular, given a specific robot type our algorithm precomputes a tiling roadmap, which efficiently and implicitly encodes the self-collision free (sub-)space over the entire configuration space, where the latter can be infinite for that matter. To answer any query, in any given scenario, we traverse the tiling roadmap while only testing for collisions with obstacles. Our algorithm suggests more flexibility than the prevailing paradigm in which a precomputed roadmap depends both on the robot and on the scenario at hand. We show through various simulations the effectiveness of this approach on open and closed-chain multi-link robots, where in some settings our algorithm is more than fifty times faster than the state-of-the-art.

Includes members:
  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-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)