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: "whole body mpc for a dynamically stable mobile manipulator"
  Abstract: Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.

Connected through hyperedge HE-1 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "object finding in cluttered scenes using interactive perception"
    - "deep value model predictive control"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "nonlinear model predictive guidance for fixed wing uavs using identified control augmented dynamics"
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep value model predictive control"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "temporal difference models model free deep rl for model based control"
    - "real time motion planning of legged robots a model predictive control approach"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "adaptive importance sampling for control and inference"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)