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: "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
  Abstract: This paper studies the underlying combinatorial structure of a class of object rearrangement problems, which appear frequently in applications. The problems involve multiple, similar-geometry objects placed on a flat, horizontal surface, where a robot can approach them from above and perform pick-and-place operations to rearrange them. The paper considers both the case where the start and goal object poses overlap, and where they do not. For overlapping poses, the primary objective is to minimize the number of pick-and-place actions and then to minimize the distance traveled by the end-effector. For the non-overlapping case, the objective is solely to minimize the travel distance of the end-effector. While such problems do not involve all the complexities of general rearrangement, they remain computationally hard in both cases. This is shown through reductions from well-understood, hard combinatorial challenges to these rearrangement problems. The reductions are also shown to hold in the reverse direction, which enables the convenient application on rearrangement of well studied algorithms. These algorithms can be very efficient in practice despite the hardness results. The paper builds on these reduction results to propose an algorithmic pipeline for dealing with the rearrangement problems. Experimental evaluation, including hardware-based trials, shows that the proposed pipeline computes high-quality paths with regards to the optimization objectives. Furthermore, it exhibits highly desirable scalability as the number of objects increases in both the overlapping and non-overlapping setup.

Connected through hyperedge HE-1 (induced by source paper "scalable and probabilistically complete planning for robotic spatial extrusion"):
  with members:
    - "freeform assembly planning"
    - "automated sequence and motion planning for robotic spatial extrusion of 3d trusses"
    - "sampling based methods for factored task and motion planning"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "backward forward search for manipulation planning"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"

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

Connected through hyperedge HE-3 (induced by source paper "integer programming as a general solution methodology for path based optimization in robotics principles best practices and applications"):
  with members:
    - "multi agent path finding with deadlines"
    - "optimal tourist problem and anytime planning of trip itineraries"
    - "incremental sampling based algorithm for minimum violation motion planning"
    - "k color multi robot motion planning"

Connected through hyperedge HE-4 (induced by source paper "toward fast and optimal robotic pick and place on a moving conveyor"):
  with members:
    - "taming combinatorial challenges in optimal clutter removal tasks"
    - "asymptotically optimal algorithms for one to one pickup and delivery problems with applications to transportation systems"

Connected through hyperedge HE-5 (induced by source paper "taming combinatorial challenges in optimal clutter removal tasks"):
  with members:
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"):
  with members:
    - "coordinating the motion of labeled discs with optimality guarantees under extreme density"
    - "scalable asymptotically optimal multi robot motion planning"
    - "characterizing minimum length coordinated motions for two discs"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "asymptotically optimal algorithms for one to one pickup and delivery problems with applications to transportation systems"
    - "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)