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 "anytime motion planning on large dense roadmaps with expensive edge evaluations"):
  Abstract: We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive. A large dense roadmap (graph) can typically ensure the existence of high quality solutions for most motion-planning problems, but the size of the roadmap, particularly in high-dimensional spaces, makes existing search-based planning algorithms computationally expensive. We deal with the challenges of expensive search and collision checking in two ways. First, we frame the problem of anytime motion planning on roadmaps as searching for the shortest path over a sequence of subgraphs of the entire roadmap graph, generated by some densification strategy. This lets us achieve bounded sub-optimality with bounded worst-case planning effort. Second, for searching each subgraph, we develop an anytime planning algorithm which uses a belief model to compute the collision probability of unknown configurations and searches for paths that are Pareto-optimal in path length and collision probability. This algorithm is efficient with respect to collision checks as it searches for successively shorter paths. We theoretically analyze both our ideas and evaluate them individually on high-dimensional motion-planning problems. Finally, we apply both of these ideas together in our algorithmic framework for anytime motion planning, and show that it outperforms BIT* on high-dimensional hypercube problems.

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 "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning":
    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 "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  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 "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs":
    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)