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: "what would you do acting by learning to predict"
  Abstract: We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perform a human demonstrated task without knowledge of the thought processes or actions of the human, only their visually observable state transitions. We evaluate our approach on two table-top, object manipulation tasks and demonstrate generalisation to previously unseen states. Our approach reduces the priors required to implement a robot task learning system compared with the existing approaches of Learning from Demonstration, Reinforcement Learning and Inverse Reinforcement Learning.

Connected through hyperedge HE-1 (induced by source paper "avid learning multi stage tasks via pixel level translation of human videos"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "third person visual imitation learning via decoupled hierarchical controller"
    - "imitating latent policies from observation"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "solar deep structured latent representations for model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "one shot imitation from observing humans via domain adaptive meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "virtual to real reinforcement learning for autonomous driving"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning to generalize meta learning for domain generalization"
    - "satellite image based localization via learned embeddings"
    - "learning robot activities from first person human videos using convolutional future regression"

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)