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 "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics"):
  Abstract: Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic. #R##N#Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial. #R##N#This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.

Includes members:
  Member "batch informed trees bit informed asymptotically optimal anytime search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advanced bit abit sampling based planning with advanced graph search techniques")
  Member "sampling based optimal kinodynamic planning with motion primitives":
    also connected through other hyperedges:
      (none)
  Member "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges":
    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 "informed sampling for asymptotically optimal path planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  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-4 (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-5 (induced by source paper "an efficient b spline based kinodynamic replanning framework for quadrotors")
  Member "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  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-7 (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)