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 "effective metrics for multi robot motion planning"):
  Abstract: We study the effectiveness of metrics for Multi-Robot Motion-Planning (MRMP) when using RRT-style sampling-based planners. These metrics play the crucial role of determining the nearest neighbors of configurations and in that they regulate the connectivity of the underlying roadmaps produced by the planners and other properties like the quality of solution paths. After screening over a dozen different metrics we focus on the five most promising ones- two more traditional metrics, and three novel ones which we propose here, adapted from the domain of shape-matching. In addition to the novel multi-robot metrics, a central contribution of this work are tools to analyze and predict the effectiveness of metrics in the MRMP context. We identify a suite of possible substructures in the configuration space, for which it is fairly easy (i) to define a so-called natural distance which allows us to predict the performance of a metric. This is done by comparing the distribution of its values for sampled pairs of configurations to the distribution induced by the natural distance; (ii) to define equivalence classes of configurations and test how well a metric covers the different classes. We provide experiments that attest to the ability of our tools to predict the effectiveness of metrics: those metrics that qualify in the analysis yield higher success rate of the planner with fewer vertices in the roadmap. We also show how combining several metrics together leads to better results (success rate and size of roadmap) than using a single metric.

Includes members:
  Member "scalable asymptotically optimal multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "on the hardness of unlabeled multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "efficient multi robot motion planning for unlabeled discs in simple polygons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  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-4 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "k color multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "on the power of manifold samples in exploring configuration spaces and the dimensionality of narrow passages":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "pspace completeness of sliding block puzzles and other problems through the nondeterministic constraint logic model of computation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the computational complexity of fire emblem series and similar tactical role playing games")

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)