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: "kernelized movement primitives"
  Abstract: Imitation learning has been studied widely as a convenient way to transfer human skills to robots. This learning approach is aimed at extracting relevant motion patterns from human demonstrations and subsequently applying these patterns to different situations. Despite many advancements have been achieved, the solutions for coping with unpredicted situations (e.g., obstacles and external perturbations) and high-dimensional inputs are still largely open. In this paper, we propose a novel kernelized movement primitive (KMP), which allows the robot to adapt the learned motor skills and fulfill a variety of additional constraints arising over the course of a task. Specifically, KMP is capable of learning trajectories associated with high-dimensional inputs due to the kernel treatment, which in turn renders a model with fewer open parameters in contrast to methods that rely on basis functions. Moreover, we extend our approach by exploiting local trajectory representations in different coordinate systems that describe the task at hand, endowing KMP with reliable extrapolation capabilities in broader domains. We apply KMP to the learning of time-driven trajectories as a special case, where a compact parametric representation describing a trajectory and its first-order derivative is utilized. In order to verify the effectiveness of our method, several examples of trajectory modulations and extrapolations associated with time inputs, as well as trajectory adaptations with high-dimensional inputs are provided.

Connected through hyperedge HE-1 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "learning novel policies for tasks"
    - "an algorithmic perspective on imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "divide and conquer reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "variational autoencoder trajectory primitives with continuous and discrete latent codes"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "neural probabilistic motor primitives for humanoid control"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "end to end training of deep visuomotor policies"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "towards orientation learning and adaptation in cartesian space"):
  with members:
    - "uncertainty aware imitation learning using kernelized movement primitives"
    - "learning to serve an experimental study for a new learning from demonstrations framework"
    - "active learning with statistical models"

Connected through hyperedge HE-4 (induced by source paper "learning from demonstration with model based gaussian process"):
  with members:
    - "probabilistic learning of torque controllers from kinematic and force constraints"
    - "small variance asymptotics for non parametric online robot learning"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "benchmark for skill learning from demonstration impact of user experience task complexity and start configuration on performance"):
  with members:
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-6 (induced by source paper "uncertainty aware imitation learning using kernelized movement primitives"):
  with members:
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "probabilistic learning of torque controllers from kinematic and force constraints"

Connected through hyperedge HE-7 (induced by source paper "a linearly constrained nonparametric framework for imitation learning"):
  with members:
    - "active learning with statistical models"

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)