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: "sequential linear quadratic optimal control for nonlinear switched systems"
  Abstract: Abstract   In this contribution, we introduce an efficient method for solving the optimal control problem for an unconstrained nonlinear switched system with an arbitrary cost function. We assume that the sequence of the switching modes are given but the switching time in between consecutive modes remains to be optimized. The proposed method uses a two-stage approach as introduced by Xu and Antsaklis (2004) where the original optimal control problem is transcribed into an equivalent problem parametrized by the switching times and the optimal control policy is obtained based on the solution of a two-point boundary value differential equation. The main contribution of this paper is to use a Sequential Linear Quadratic approach to synthesize the optimal controller instead of solving a boundary value problem. The proposed method is numerically more efficient and scales very well to the high dimensional problems. In order to evaluate its performance, we use two numerical examples as benchmarks to compare against the baseline algorithm. In the third numerical example, we apply the proposed algorithm to the Center of Mass control problem in a quadruped robot locomotion task.

Connected through hyperedge HE-1 (induced by source paper "adapt zero shot adaptive policy transfer for stochastic dynamical systems"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "progressive neural networks"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "epopt learning robust neural network policies using model ensembles"
    - "continuous deep q learning with model based acceleration"
    - "plato policy learning using adaptive trajectory optimization"

Connected through hyperedge HE-2 (induced by source paper "locomotion planning through a hybrid bayesian trajectory optimization"):
  with members:
    - "learning a structured neural network policy for a hopping task"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "reactive planar manipulation with convex hybrid mpc"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"

Connected through hyperedge HE-3 (induced by source paper "real time motion planning of legged robots a model predictive control approach"):
  with members:
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"

Connected through hyperedge HE-4 (induced by source paper "an efficient optimal planning and control framework for quadrupedal locomotion"):
  with members:
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "projection based whole body motion planning for legged robots"

Connected through hyperedge HE-5 (induced by source paper "robust whole body motion control of legged robots"):
  with members:
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"

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)