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: "simple kinematic feedback enhances autonomous learning in bio inspired tendon driven systems"
  Abstract: Error feedback is known to improve performance by correcting control signals in response to perturbations. Here we show how adding simple error feedback can also accelerate and robustify autonomous learning in robots. We implemented two versions of the General-to-Particular (G2P) autonomous learning algorithm to produce multiple movement tasks using a tendon-driven leg with two joints and three tendons: one with and one without kinematic feedback. As expected, feedback improved performance in simulation and hardware. However, we see these improvements even in the presence of sensory delays of up to 100 ms and when experiencing substantial contact collisions. Importantly, feedback accelerates learning by enhancing G2P's continual refinement of the initial inverse map because every experience counts. This allows the system to perform well even after only 60 seconds of initial motor babbling.

Connected through hyperedge HE-1 (induced by source paper "the utility of tactile force to autonomous learning of in hand manipulation is task dependent"):
  with members:
    - "autonomous control of a tendon driven robotic limb with elastic elements reveals that added elasticity can enhance learning"
    - "learning dexterous in hand manipulation"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "autonomous control of a tendon driven robotic limb with elastic elements reveals that added elasticity can enhance learning"):
  with members:
    - "data efficient reinforcement learning for legged robots"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "continuous control with deep reinforcement learning"
    - "robots that can adapt like animals"

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)