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: "automatic differentiation of rigid body dynamics for optimal control and estimation"
  Abstract: Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool `RobCoGen' to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning.

Connected through hyperedge HE-1 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-2 (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"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"

Connected through hyperedge HE-3 (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"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-4 (induced by source paper "physics based simulation of continuous wave lidar for localization calibration and tracking"):
  with members:
    - "interactive differentiable simulation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "the stan math library reverse mode automatic differentiation in c"

Connected through hyperedge HE-5 (induced by source paper "a fully integrated sensing and control system for high accuracy mobile robotic building construction"):
  with members:
    - "confusion sensor fusion for complex robotic systems using nonlinear optimization"
    - "real time motion planning of legged robots a model predictive control approach"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "whole body nonlinear model predictive control through contacts for quadrupeds"):
  with members:
    - "the control toolbox an open source c library for robotics optimal and model predictive control"
    - "a family of iterative gauss newton shooting methods for nonlinear optimal control"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "hierarchical planning of dynamic movements without scheduled contact sequences"
    - "projection based whole body motion planning for legged robots"
    - "planning and execution of dynamic whole body locomotion for a hydraulic quadruped on challenging terrain"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-7 (induced by source paper "the control toolbox an open source c library for robotics optimal and model predictive control"):
  with members:
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "a family of iterative gauss newton shooting methods for nonlinear optimal control"
    - "efficient kinematic planning for mobile manipulators with non holonomic constraints using optimal control"
    - "blasfeo basic linear algebra subroutines for embedded optimization"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "odeint solving ordinary differential equations in c"

Connected through hyperedge HE-8 (induced by source paper "structured neural network dynamics for model based control"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "model based control using koopman operators"
    - "embed to control a locally linear latent dynamics model for control from raw images"

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)