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: "linear combination of one step predictive information with an external reward in an episodic policy gradient setting a critical analysis"
  Abstract: One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost.

Connected through hyperedge HE-1 (induced by source paper "human perception of intrinsically motivated autonomy in human robot interaction"):
  with members:
    - "intrinsically motivated autonomy in human robot interaction human perception of predictive information in robots"
    - "utilizing bluetooth low energy to recognize proximity touch and humans"

Connected through hyperedge HE-2 (induced by source paper "control what you can intrinsically motivated task planning agent"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "relational deep reinforcement learning"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "deep reinforcement learning for event triggered control"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "nmode neuro module evolution"):
  with members:
    - "a theory of cheap control in embodied systems"

Connected through hyperedge HE-4 (induced by source paper "vime variational information maximizing exploration"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "information theoretically aided reinforcement learning for embodied agents"
    - "benchmarking deep reinforcement learning for continuous control"
    - "action conditional video prediction using deep networks in atari games"
    - "trust region policy optimization"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "bayesian reinforcement learning a survey"
    - "adam a method for stochastic optimization"

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)