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 imitation from observing humans via domain adaptive meta learning"
  Abstract: Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning from a raw video pixels of a human, even when there is substantial domain shift in the perspective, environment, and embodiment between the robot and the observed human. Prior approaches to this problem have hand-specified how human and robot actions correspond and often relied on explicit human pose detection systems. In this work, we present an approach for one-shot learning from a video of a human by using human and robot demonstration data from a variety of previous tasks to build up prior knowledge through meta-learning. Then, combining this prior knowledge and only a single video demonstration from a human, the robot can perform the task that the human demonstrated. We show experiments on both a PR2 arm and a Sawyer arm, demonstrating that after meta-learning, the robot can learn to place, push, and pick-and-place new objects using just one video of a human performing the manipulation.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "machine learning approaches for motor learning a short review"):
  with members:
    - "human motion trajectory prediction a survey"
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "motion prediction with recurrent neural network dynamical models and trajectory optimization"
    - "generative adversarial self imitation learning"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "convolutional sequence to sequence model for human dynamics"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-3 (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning"):
  with members:
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "self supervised correspondence in visuomotor policy learning"
    - "learning representations by maximizing mutual information across views"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning actionable representations from visual observations"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "scalable multi task imitation learning with autonomous improvement"):
  with members:
    - "unsupervised visuomotor control through distributional planning networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "soft actor critic algorithms and applications"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "task embedded control networks for few shot imitation learning"
    - "hindsight experience replay"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-5 (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 observation learning using visual activity features"
    - "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"

Connected through hyperedge HE-6 (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 hierarchical imitation learning of compound visuomotor tasks"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-7 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-8 (induced by source paper "scene understanding for autonomous manipulation with deep learning"):
  with members:
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "visual affordance and function understanding a survey"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "video captioning with transferred semantic attributes"
    - "weakly supervised learning of affordances"

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)