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 hierarchical imitation learning of compound visuomotor tasks"
  Abstract: We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to provide, but do not provide any direct supervision. Learning policies from raw pixels enables full generality but calls for large function approximators with many parameters to be learned. Finally, compound tasks can require impractical amounts of demonstration data, when treated as a monolithic skill. To address these challenges, we propose a method that learns both how to learn primitive behaviors from video demonstrations and how to dynamically compose these behaviors to perform multi-stage tasks by "watching" a human demonstrator. Our results on a simulated Sawyer robot and real PR2 robot illustrate our method for learning a variety of order fulfillment and kitchen serving tasks with novel objects and raw pixel inputs.

Connected through hyperedge HE-1 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "visual robot task planning"
    - "learning generalized reactive policies using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "squirl robust and efficient learning from video demonstration of long horizon robotic manipulation tasks"):
  with members:
    - "meta inverse reinforcement learning with probabilistic context variables"
    - "learning latent plans from play"
    - "a divergence minimization perspective on imitation learning methods"
    - "motion reasoning for goal based imitation learning"
    - "deepmind control suite"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-3 (induced by source paper "a robot that learns connect four using game theory and demonstrations"):
  with members:
    - "few shot learning with meta metric learners"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "meta learning for semi supervised few shot classification"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning game representations from data using rationality constraints"

Connected through hyperedge HE-4 (induced by source paper "meta inverse reinforcement learning with probabilistic context variables"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "multi task maximum entropy inverse reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning to optimize neural nets"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-5 (induced by source paper "to follow or not to follow selective imitation learning from observations"):
  with members:
    - "unsupervised visuomotor control through distributional planning networks"
    - "ikea furniture assembly environment for long horizon complex manipulation tasks"
    - "universal planning networks"
    - "zero shot visual imitation"
    - "sfv reinforcement learning of physical skills from videos"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-6 (induced by source paper "a behavioral approach to visual navigation with graph localization networks"):
  with members:
    - "semi parametric topological memory for navigation"
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "relational inductive biases deep learning and graph networks"
    - "visual semantic navigation using scene priors"
    - "how powerful are graph neural networks"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-7 (induced by source paper "meta adaptation using importance weighted demonstrations"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "shared multi task imitation learning for indoor self navigation"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "hierarchical imitation and reinforcement learning"
    - "learning to reweight examples for robust deep learning"
    - "recurrent world models facilitate policy evolution"
    - "meta learning framework for automated driving"

Connected through hyperedge HE-8 (induced by source paper "learning from interventions using hierarchical policies for safe learning"):
  with members:
    - "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "cycle of learning for autonomous systems from human interaction"
    - "navigation by imitation in a pedestrian rich environment"
    - "hierarchical imitation and reinforcement learning"
    - "hindsight experience replay"
    - "carla an open urban driving simulator"
    - "curiosity driven exploration by self supervised prediction"

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)