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 "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs"):
  Abstract: Discrete and sampling-based methods have traditionally been popular techniques for path planning in continuous spaces. Discrete techniques use the principles of dynamic programming to solve a discretized approximation of the problem, while sampling-based techniques use random samples to perform a stochastic search on the continuous state space. #R##N#In this paper, we use the fact that stochastic planners can be viewed as a search of an implicit random geometric graph (RGG) to propose a general class of planners named Bellman Random Trees (BRT) and derive an anytime optimal sampling-based algorithm, Batch Informed Trees (BIT*). #R##N#BIT* searches for a solution to the continuous planning problem by efficiently building and searching a series of implicit RGGs in a principled manner. In doing so, it strikes a balance between the advantages of discrete methods and sampling-based planners. By using the implicit RGG representation, defined as set of random samples and successor function, BIT* is able to scale more effectively to high dimensions than other optimal sampling-based planners. By using heuristics and intelligently reusing existing connections, like discrete lifelong planning algorithms, BIT* is able to focus its search in a principled and efficient manner. #R##N#In simulations, we show that BIT* consistently outperforms Optimal RRT (RRT*), Informed RRT*, and Fast Marching Trees (FMT*) on random-world problems in $\mathbb{R}^2$ and $\mathbb{R}^8$ . We also present preliminary theoretical analysis demonstrating that BIT* is probabilistically complete and asymptotically optimal and conjecture that it may be optimally efficient under some probabilistic conditions.

Includes members:
  Member "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  Member "fast marching tree a fast marching sampling based method for optimal motion planning in many dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "informed asymptotically optimal anytime search")
  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)