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: "automated motion planning for robotic assembly of discrete architectural structures"
  Abstract: While robotics for architectural-scale construction has made significant progress in recent years, a major challenge remains in automatically planning robotic motion for the assembly of complex structures. This paper proposes a new hierarchical planning framework to solve the assembly planning problem for architectural discrete structures, which usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the assembly sequence, end-effector poses, joint configurations, and transition trajectories for assembly of spatial structures with nonstandard topologies, which hasn't been demonstrated in previous literature. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine and application-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented to show Choreo's application on spatial extrusion.

Connected through hyperedge HE-1 (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"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "rearrangement with nonprehensile manipulation using deep reinforcement learning"
    - "efficient high quality stack rearrangement"
    - "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)