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 "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges"):
  Abstract: Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face of collision uncertainty minimizes the number of checks before finding the shortest path. However, this may take a prohibitively long time to compute, with no other feasible paths discovered during this period. For many real-time applications, we instead demand strong anytime performance, defined as minimizing the cumulative lengths of the feasible paths yielded over time. We introduce Posterior Sampling for Motion Planning (PSMP), an anytime lazy motion planning algorithm that leverages learned posteriors on edge collisions to quickly discover an initial feasible path and progressively yield shorter paths. PSMP obtains an expected regret bound of $\tilde{O}(\sqrt{\mathcal{S} \mathcal{A} T})$ and outperforms comparative baselines on a set of 2D and 7D planning problems.

Includes members:
  Member "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward asymptotically optimal inspection planning via efficient near optimal graph search")
  Member "leveraging experience in lazy search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable gaussian process motion planning")
  Member "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges":
    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 "motion planning networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "bayesian active edge evaluation on expensive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leveraging experience in lazy search")
  Member "near optimal edge evaluation in explicit generalized binomial graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "leveraging experience in lazy search")
  Member "pomdp lite for robust robot planning under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts")
  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-8 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")

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)