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 "densification strategies for anytime motion planning over large dense roadmaps"):
  Abstract: We consider the problem of computing shortest paths in a dense motion-planning roadmap $\mathcal{G}$. We assume that $n$, the number of vertices of $\mathcal{G}$, is very large. Thus, using any path-planning algorithm that directly searches $\mathcal{G}$, running in $O(n^2)$ time, becomes unacceptably expensive. We are therefore interested in anytime search algorithms that obtain successively shorter feasible paths and converge to the shortest path in $\mathcal{G}$. Our key insight is to leverage existing path-planning algorithms and provide them with a sequence of subgraphs of $\mathcal{G}$. To do so, we study the space of all ($r$-disk) subgraphs of $\mathcal{G}$. We then formulate and present two densification strategies for traversing this space which exhibit complementary properties with respect to problem difficulty. This inspires a third, hybrid strategy which has favourable properties regardless of problem difficulty. This general approach is then demonstrated and analyzed using the specific case where a low-dispersion deterministic sequence is used to generate the samples used for $\mathcal{G}$. Finally we empirically evaluate the performance of our strategies for random scenarios in $\mathbb{R}^{2}$ and $\mathbb{R}^{4}$ and on manipulation planning problems for a 7 DOF robot arm, and validate our analysis.

Includes members:
  Member "new perspective on sampling based motion planning via random geometric graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sample complexity of probabilistic roadmaps via e nets")
  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 "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  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-4 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "asymptotically optimal motion planning using lower bounds on cost":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  Member "asymptotically near optimal rrt for fast high quality motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  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-8 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")

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)