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: "intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks"
  Abstract: Abstract   Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signal cognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.

Connected through hyperedge HE-1 (induced by source paper "curious ilqr resolving uncertainty in model based rl"):
  with members:
    - "model based active exploration"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "curiosity driven exploration by self supervised prediction"
    - "risk sensitive nonlinear optimal control with measurement uncertainty"
    - "unifying count based exploration and intrinsic motivation"
    - "risk sensitive nonlinear optimal control iterative linear exponential quadratic optimal control with gaussian noise"

Connected through hyperedge HE-2 (induced by source paper "autonomous reinforcement learning of multiple interrelated tasks"):
  with members:
    - "autonomous discovery of the goal space to learn a parameterized skill"
    - "automatic goal generation for reinforcement learning agents"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

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.AI(Artificial Intelligence)