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: "memory of motion for warm starting trajectory optimization"
  Abstract: Trajectory optimization for motion planning requires a good initial guess to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide a good initial guess online. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the task and the robot paths. Three function approximators are compared: k-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the usage of the memory of motion can be improved by using an ensemble method, and that the memory can also be used as a metric to choose between several possible goals. We demonstrate the proposed approach with the motion planning on a dual-arm PR2 robot.

Connected through hyperedge HE-1 (induced by source paper "learning how to walk warm starting optimal control solver with memory of motion"):
  with members:
    - "crocoddyl an efficient and versatile framework for multi contact optimal control"
    - "passive whole body control for quadruped robots experimental validation over challenging terrain"
    - "differential dynamic programming for multi phase rigid contact dynamics"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "planning and execution of dynamic whole body locomotion for a hydraulic quadruped on challenging terrain"

Connected through hyperedge HE-2 (induced by source paper "a memory of motion for visual predictive control tasks"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"

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)