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 "sample complexity of probabilistic roadmaps via e nets"):
  Abstract: We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in the finite time (non-asymptotic) regime. In particular, we investigate how completeness and optimality guarantees of the approach are influenced by the underlying deterministic sampling distribution ${\X}$ and connection radius ${r>0}$. We develop the notion of ${(\delta,\epsilon)}$-completeness of the parameters ${\X, r}$, which indicates that for every motion-planning problem of clearance at least ${\delta>0}$, PRM using ${\X, r}$ returns a solution no longer than ${1+\epsilon}$ times the shortest ${\delta}$-clear path. Leveraging the concept of ${\epsilon}$-nets, we characterize in terms of lower and upper bounds the number of samples needed to guarantee ${(\delta,\epsilon)}$-completeness. This is in contrast with previous work which mostly considered the asymptotic regime in which the number of samples tends to infinity. In practice, we propose a sampling distribution inspired by ${\epsilon}$-nets that achieves nearly the same coverage as grids while using significantly fewer samples.

Includes members:
  Member "drrt scalable and informed asymptotically optimal multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotically optimal sampling based planners")
  Member "toward asymptotically optimal inspection planning via efficient near optimal graph search":
    also connected through other hyperedges:
      (none)
  Member "robot motion planning in learned latent spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "fast anytime motion planning for prehensile manipulation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asymptotically optimal sampling based planners")
  Member "ffrob leveraging symbolic planning for efficient task and motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "learning sampling distributions for robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating cooperative planning for automated vehicles with learned heuristics and monte carlo tree search")
  Member "the critical radius in sampling based motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pushing the boundaries of asymptotic optimality for sampling based roadmaps in motion and task planning")
  Member "data driven planning via imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts")

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.DS(Data Structures and Algorithms)