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 learning of multi step tasks from observation via activity localization in auxiliary video"
  Abstract: Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots. Demonstrations are inherently ambiguous and incomplete, making correct generalization to unseen situations difficult without a large number of demonstrations in varying conditions. By contrast, humans are often able to learn complex tasks from a single demonstration (typically observations without action labels) by leveraging context learned over a lifetime. Inspired by this capability, our goal is to enable robots to perform one-shot learning of multi-step tasks from observation by leveraging auxiliary video data as context. Our primary contribution is a novel system that achieves this goal by: (1) using a single user-segmented demonstration to define the primitive actions that comprise a task, (2) localizing additional examples of these actions in unsegmented auxiliary videos via a metalearning-based approach, (3) using these additional examples to learn a reward function for each action, and (4) performing reinforcement learning on top of the inferred reward functions to learn action policies that can be combined to accomplish the task. We empirically demonstrate that a robot can learn multi-step tasks more effectively when provided auxiliary video, and that performance greatly improves when localizing individual actions, compared to learning from unsegmented videos.

Connected through hyperedge HE-1 (induced by source paper "recent advances in imitation learning from observation"):
  with members:
    - "hybrid reinforcement learning with expert state sequences"
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "3d human pose machines with self supervised learning"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "internal model from observations for reward shaping"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-2 (induced by source paper "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "inverse reinforcement learning for video games"
    - "playing hard exploration games by watching youtube"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "generative adversarial imitation from observation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"

Connected through hyperedge HE-3 (induced by source paper "imitation learning from video by leveraging proprioception"):
  with members:
    - "recent advances in imitation learning from observation"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "an algorithmic perspective on imitation learning"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "generative adversarial imitation from observation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "behavioral cloning from observation"

Connected through hyperedge HE-4 (induced by source paper "plots procedure learning from observations using subtask structure"):
  with members:
    - "composable planning with attributes"
    - "reinforcement learning on web interfaces using workflow guided exploration"
    - "taco learning task decomposition via temporal alignment for control"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "ddco discovery of deep continuous options for robot learning from demonstrations"

Connected through hyperedge HE-5 (induced by source paper "continuous relaxation of symbolic planner for one shot imitation learning"):
  with members:
    - "learning quickly to plan quickly using modular meta learning"
    - "zero shot visual imitation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "taco learning task decomposition via temporal alignment for control"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "one shot visual imitation learning via meta learning"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "learning heuristic search via imitation"

Connected through hyperedge HE-6 (induced by source paper "understanding teacher gaze patterns for robot learning"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "digging deeper into egocentric gaze prediction"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "compact generalized non local network"
    - "non local neural networks"

Connected through hyperedge HE-7 (induced by source paper "neural task graphs generalizing to unseen tasks from a single video demonstration"):
  with members:
    - "visual coreference resolution in visual dialog using neural module networks"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "inferring and executing programs for visual reasoning"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning to reason end to end module networks for visual question answering"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "dense captioning events in videos"

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.LG(Machine Learning)