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: "one shot observation learning using visual activity features"
  Abstract: Observation learning is the process of learning a task by observing an expert demonstrator. Our principal contribution is a one-shot learning method for robot manipulation tasks in which only a single demonstration is required. The key idea is to encode the demonstration in an activity space defined as part of a previously trained activity classifier. The distance between this encoding and equivalent encodings from trials of a robot performing the same task provides a reward function supporting iterative learning of task completion by the robotic manipulator. We use reinforcement learning for experiments with a simulated robotic manipulator, and stochastic trajectory optimisation for experiments with a real robotic manipulator. We show that the proposed method can be used to learn tasks from a single demonstration under varying viewpoint of observation, object properties, scene background and morphology of the manipulator. Videos of all results, including demonstrations, can be found on: this https URL

Connected through hyperedge HE-1 (induced by source paper "vision based robot manipulation learning via human demonstrations"):
  with members:
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "learning actions from human demonstration video for robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "intel realsense stereoscopic depth cameras"

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)