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: "fractal impedance for passive controllers"
  Abstract: During the last decade, there has been increasing interest in new control frameworks to move robots from their industrial cages to unstructured environments where they may coexist with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need of a general control framework to improve the robots' dynamics interaction performance without limiting system safety. The passive control framework has shown promising results in this direction; however, it relies on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a fractal attractor is proposed to implement a variable impedance controller that is able to retain passivity without relying on the energy tank framework. The results prove that the proposed method can accurately track trajectories and apply forces at the end-effector. Furthermore, it can automatically deal with the extra energy introduced by changes in interaction conditions, null-space controller and environment. Therefore, These properties make the controller ideal for applications where the dynamic interaction at the end-effector is difficult to be characterized in advance, such as human-robot interaction and unknown dynamics.

Connected through hyperedge HE-1 (induced by source paper "bio mimetic adaptive force position control using fractal impedance"):
  with members:
    - "safe and compliant control of redundant robots using a stack of passive task space controllers"

Connected through hyperedge HE-2 (induced by source paper "safe and compliant control of redundant robots using a stack of passive task space controllers"):
  with members:
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"

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)