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 "interactive trajectory adaptation through force guided bayesian optimization"):
  Abstract: Flexible manufacturing processes demand robots to easily adapt to changes in the environment and interact with humans. In such dynamic scenarios, robotic tasks may be programmed through learning-from-demonstration approaches, where a nominal plan of the task is learned by the robot. However, the learned plan may need to be adapted in order to fulfill additional requirements or overcome unexpected environment changes. When the required adaptation occurs at the end-effector trajectory level, a human operator may want to intuitively show the robot the desired changes by physically interacting with it. In this scenario, the robot needs to understand the human intended changes from noisy haptic data, quickly adapt accordingly and execute the nominal task plan when no further adaptation is needed. This paper addresses the aforementioned challenges by leveraging LfD and Bayesian optimization to endow the robot with data-efficient adaptation capabilities. Our approach exploits the sensed interaction forces to guide the robot adaptation, and speeds up the optimization process by defining local search spaces extracted from the learned task model. We show how our framework quickly adapts the learned spatial-temporal patterns of the task, leading to deformed trajectory distributions that are consistent with the nominal plan and the changes introduced by the human.

Includes members:
  Member "trajectory deformations from physical human robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections")
  Member "deep kernels for optimizing locomotion controllers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "botorch programmable bayesian optimization in pytorch")
  Member "mathematical models of adaptation in human robot collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to switch between machines and humans")
  Member "learning dynamic robot to human object handover from human feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning")
  Member "a sensorimotor reinforcement learning framework for physical human robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning")
  Member "automatic lqr tuning based on gaussian process global optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the racing line using bayesian optimization")
  Member "safe controller optimization for quadrotors with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "robots that can adapt like animals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")

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)