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 "minimizing task space frechet error via efficient incremental graph search"):
  Abstract: We present an algorithm that generates a collision-free configuration-space path that closely follows, according to the discrete Fr\'echet metric, a desired path in task space. By leveraging the Fr\'echet metric and other tools from computational geometry, we approximate the search space using a cross-product graph. This structure allows us to efficiently search for the solution using a simple variant of Dijkstra's graph search algorithm. Additionally, we can incrementally update and improve the solution in an anytime fashion. We compare multiple proposed densification strategies and show that our algorithm outperforms a previously proposed optimization-based approach.

Includes members:
  Member "the provable virtue of laziness in motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast planning over roadmaps via selective densification")
  Member "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "sampling based bottleneck pathfinding with applications to frechet matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)