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 deep parameterized skills from demonstration for re targetable visuomotor control"
  Abstract: Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every different goal or do not infer the specific target in the presence of multiple goals from visual data. We introduce an end-to-end method that represents targetable visuomotor skills as a goal-parameterized neural network policy. By training on an informative subset of available goals with the associated target parameters, we are able to learn a policy that can zero-shot generalize to previously unseen goals. We evaluate our method in a representative 2D simulation of a button-grid and on both button-pressing and peg-insertion tasks on two different physical arms. We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments. We also successfully learn a mapping from target pixel coordinates to a robot policy to complete a specified goal.

Connected through hyperedge HE-1 (induced by source paper "learning to generalize across long horizon tasks from human demonstrations"):
  with members:
    - "iris implicit reinforcement without interaction at scale for learning control from offline robot manipulation data"
    - "learning latent plans from play"
    - "deep local trajectory replanning and control for robot navigation"
    - "avid learning multi stage tasks via pixel level translation of human videos"
    - "scaling robot supervision to hundreds of hours with roboturk robotic manipulation dataset through human reasoning and dexterity"
    - "learning to optimize join queries with deep reinforcement learning"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"

Connected through hyperedge HE-2 (induced by source paper "imitation learning of robot policies by combining language vision and demonstration"):
  with members:
    - "disentangled relational representations for explaining and learning from demonstration"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "one shot visual imitation learning via meta learning"
    - "deep residual learning for image recognition"
    - "stacked attention networks for image question answering"

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)