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: "learning dynamic robot to human object handover from human feedback"
  Abstract: Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.

Connected through hyperedge HE-1 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "interactive trajectory adaptation through force guided bayesian optimization"):
  with members:
    - "trajectory deformations from physical human robot interaction"
    - "deep kernels for optimizing locomotion controllers"
    - "mathematical models of adaptation in human robot collaboration"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "robots that can adapt like animals"

Connected through hyperedge HE-3 (induced by source paper "dueling posterior sampling for preference based reinforcement learning"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "multi dueling bandits with dependent arms"
    - "concrete problems in ai safety"
    - "contextual dueling bandits"

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)