human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "efficient high quality stack rearrangement"
  Abstract: This work studies rearrangement problems involving the sorting of robots or objects in stack-like containers, which can be accessed only from one side. Two scenarios are considered: one where every robot or object needs to reach a particular stack, and a setting in which each robot has a distinct position within a stack. In both cases, the goal is to minimize the number of stack removals that need to be performed. Stack rearrangement is shown to be intimately connected to pebble motion problems, a useful abstraction in multi-robot path planning. Through this connection, feasibility of stack rearrangement can be readily addressed. The paper continues to establish lower and upper bounds on optimality, which differ only by a logarithmic factor, in terms of stack removals. An algorithmic solution is then developed that produces suboptimal paths much quicker than a pebble motion solver. Furthermore, informed search-based methods are proposed for finding high-quality solutions. The efficiency and desirable scalability of the methods is demonstrated in simulation.

Connected through hyperedge HE-1 (induced by source paper "on rearrangement of items stored in stacks"):
  with members:
    - "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "on the hardness of unlabeled multi robot motion planning"
    - "pushpush and push 1 are np hard in 2d"

Connected through hyperedge HE-2 (induced by source paper "object rearrangement with nested nonprehensile manipulation actions"):
  with members:
    - "towards robust product packing with a minimalistic end effector"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "sampling based methods for factored task and motion planning"
    - "automated motion planning for robotic assembly of discrete architectural structures"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "rearrangement with nonprehensile manipulation using deep reinforcement learning"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Question: Please tell me which class the center node 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)