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 nonlinear model predictive control through contacts for quadrupeds"
  Abstract: In this letter, we present a whole-body nonlinear model predictive control approach for rigid body systems subject to contacts. We use a full-dynamic system model which also includes explicit contact dynamics. Therefore, contact locations, sequences, and timings are not prespecified but optimized by the solver. Yet, using numerical and software engineering allows for running the nonlinear Optimal Control solver at rates up to 190 Hz on a quadruped for a time horizon of half a second. This outperforms the state-of-the-art by at least one order of magnitude. Hardware experiments in the form of periodic and nonperiodic tasks are applied to two quadrupeds with different actuation systems. The obtained results underline the performance, transferability, and robustness of the approach.

Connected through hyperedge HE-1 (induced by source paper "guided constrained policy optimization for dynamic quadrupedal robot locomotion"):
  with members:
    - "robust recovery controller for a quadrupedal robot using deep reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "quantifying generalization in reinforcement learning"
    - "behavioral cloning from observation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "constrained policy optimization"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "deep value model predictive control"
    - "data driven economic nmpc using reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "learning based model predictive control for safe exploration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "cautious model predictive control using gaussian process regression"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "whole body walking generation using contact parametrization a non linear trajectory optimization approach"):
  with members:
    - "a control architecture with online predictive planning for position and torque controlled walking of humanoid robots"
    - "a benchmarking of dcm based architectures for position and velocity controlled walking of humanoid robots"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "real time motion planning of legged robots a model predictive control approach"
    - "straight leg walking through underconstrained whole body control"
    - "a receding horizon push recovery strategy for balancing the icub humanoid robot"
    - "when to make a step tackling the timing problem in multi contact locomotion by topp mpc"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-4 (induced by source paper "data efficient reinforcement learning for legged robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "when to trust your model model based policy optimization"
    - "policies modulating trajectory generators"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "feedback control for cassie with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-5 (induced by source paper "combining coarse and fine physics for manipulation using parallel in time integration"):
  with members:
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-6 (induced by source paper "mpc net a first principles guided policy search"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "data efficient reinforcement learning for legged robots"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "on the convergence of adam and beyond"
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "an algorithmic perspective on imitation learning"
    - "sim to real learning agile locomotion for quadruped robots"

Connected through hyperedge HE-7 (induced by source paper "parareal with a learned coarse model for robotic manipulation"):
  with members:
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "parallelizing over artificial neural network training runs with multigrid"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-8 (induced by source paper "learning a family of optimal state feedback controllers"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "plato policy learning using adaptive trajectory optimization"
    - "adam a method for stochastic optimization"

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)