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: "grasp selection analysis for two step manipulation tasks"
  Abstract: Manipulation tasks are sequential in nature. Grasp selection approaches that take into account the con- straints at each task step are critical, since they allow to both (1) Identify grasps that likely require simple arm motions through the whole task and (2) To discard grasps that, although feasible to achieve at earlier steps, might not be executable at later stages due to goal task constraints. In this paper, we study how to use our previously proposed manipulation metric for tasks in which 2 steps are required (pick-and-place and pouring tasks). Even for such simple tasks, it was not clear how to use the results of applying our metric (or any metric for that matter) to rank all the candidate grasps: Should only the start state be considered, or only the goal, or a combination of both? In order to find an answer, we evaluated the (best) grasps selected by our metric under each of these 3 considerations. Our main conclusion was that for tasks in which the goal state is more constrained (pick-and-place), using a combination of the metric measured at the start and goal states renders better performance when compared with choosing any other candidate grasp, whereas in tasks in which the goal constraints are less rigidly defined, the metric measured at the start state should be mainly considered. We present quantitative results in simulation and validate our approach's practicality with experimental results in our physical robot manipulator, Crichton.

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)