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: "deep intrinsically motivated continuous actor critic for efficient robotic visuomotor skill learning"
  Abstract: In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input, while the centre-most hidden representation is also optimized to estimate the state value. Separately, an ensemble of predictive world models generates, based on its learning progress, an intrinsic reward signal which is combined with the extrinsic reward to guide the exploration of the actor-critic learner. Our approach is more data-efficient and inherently more stable than the existing actor-critic methods for continuous control from pixel data. We evaluate our algorithm for the task of learning robotic reaching and grasping skills on a realistic physics simulator and on a humanoid robot. The results show that the control policies learned with our approach can achieve better performance than the compared state-of-the-art and baseline algorithms in both dense-reward and challenging sparse-reward settings.

Connected through hyperedge HE-1 (induced by source paper "curious meta controller adaptive alternation between model based and model free control in deep reinforcement learning"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "hindsight experience replay"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "curiosity driven exploration by self supervised prediction"
    - "imagination augmented agents for deep reinforcement learning"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "self correcting models for model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "efficient intrinsically motivated robotic grasping with learning adaptive imagination in latent space"):
  with members:
    - "curious meta controller adaptive alternation between model based and model free control in deep reinforcement learning"
    - "hierarchical reinforcement learning with hindsight"
    - "count based exploration with the successor representation"
    - "world models"
    - "hindsight experience replay"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"
    - "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)