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: "an efficient optimal planning and control framework for quadrupedal locomotion"
  Abstract: In this paper, we present an efficient Dynamic Programing framework for optimal planning and control of legged robots. First we formulate this problem as an optimal control problem for switched systems. Then we propose a multi-level optimization approach to find the optimal switching times and the optimal continuous control inputs. Through this scheme, the decomposed optimization can potentially be done more efficiently than the combined approach. Finally, we present a continuous-time constrained LQR algorithm which simultaneously optimizes the feedforward and feedback controller with O(n) time-complexity. In order to validate our approach, we show the performance of our framework on a quadrupedal robot. We choose the Center of Mass dynamics and the full kinematic formulation as the switched system model where the switching times as well as the contact forces and the joint velocities are optimized for different locomotion tasks such as gap crossing, walking and trotting.

Connected through hyperedge HE-1 (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-2 (induced by source paper "learning agile and dynamic motor skills for legged robots"):
  with members:
    - "learning symmetric and low energy locomotion"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "feedback control for cassie with deep reinforcement learning"
    - "control of a quadrotor with reinforcement learning"
    - "fast trajectory optimization for legged robots using vertex based zmp constraints"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-3 (induced by source paper "whole body mpc for a dynamically stable mobile manipulator"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "frequency aware model predictive control"
    - "hierarchical optimization for whole body control of wheeled inverted pendulum humanoids"
    - "a whole body model predictive control scheme including external contact forces and com height variations"
    - "a mpc walking framework with external contact forces"
    - "real time motion planning of legged robots a model predictive control approach"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"

Connected through hyperedge HE-4 (induced by source paper "crocoddyl an efficient and versatile framework for multi contact optimal control"):
  with members:
    - "differential dynamic programming for multi phase rigid contact dynamics"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "motion planning for quadrupedal locomotion coupled planning terrain mapping and whole body control"
    - "a family of iterative gauss newton shooting methods for nonlinear optimal control"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "balancing experiments on a torque controlled humanoid with hierarchical inverse dynamics"

Connected through hyperedge HE-5 (induced by source paper "deep value model predictive control"):
  with members:
    - "whole body mpc for a dynamically stable mobile manipulator"
    - "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"
    - "adaptive importance sampling for control and inference"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "mpc based controller with terrain insight for dynamic legged locomotion"):
  with members:
    - "fast and continuous foothold adaptation for dynamic locomotion through cnns"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-7 (induced by source paper "feedback mpc for torque controlled legged robots"):
  with members:
    - "sensitivity of legged balance control to uncertainties and sampling period"
    - "frequency aware model predictive control"
    - "real time motion planning of legged robots a model predictive control approach"
    - "robot impedance control and passivity analysis with inner torque and velocity feedback loops"
    - "balancing and walking using full dynamics lqr control with contact constraints"

Connected through hyperedge HE-8 (induced by source paper "locomotion planning through a hybrid bayesian trajectory optimization"):
  with members:
    - "learning a structured neural network policy for a hopping task"
    - "sequential linear quadratic optimal control for nonlinear switched systems"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "reactive planar manipulation with convex hybrid mpc"

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.SY(Systems and Control)