human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "inverse kinematics for control of tensegrity soft robots existence and optimality of solutions"):
  Abstract: Tension-network (`tensegrity') robots encounter many control challenges as articulated soft robots, due to the structures' high-dimensional nonlinear dynamics. Control approaches have been developed which use the inverse kinematics of tensegrity structures, either for open-loop control or as equilibrium inputs for closed-loop controllers. However, current formulations of the tensegrity inverse kinematics problem are limited in robotics applications: first, they can lead to higher than needed cable tensions, and second, may lack solutions when applied to robots with high node-to-cable ratios. This work provides progress in both directions. To address the first limitation, the objective function for the inverse kinematics optimization problem is modified to produce cable tensions as low or lower than before, thus reducing the load on the robots' motors. For the second, a reformulation of the static equilibrium constraint is proposed, which produces solutions independent of the number of nodes within each rigid body. Simulation results using the second reformulation on a specific tensegrity spine robot show reasonable open-loop control results, whereas the previous formulation could not produce any solution.

Includes members:
  Member "model predictive control with reference input tracking for tensegrity spine robots":
    also connected through other hyperedges:
      (none)
  Member "deep reinforcement learning for tensegrity robot locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to pick the domain randomization parameters for sim to real transfer of reinforcement learning policies")

Question: Please tell me which class the source paper 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)