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: "information theoretically aided reinforcement learning for embodied agents"
  Abstract: Reinforcement learning for embodied agents is a challenging problem. The accumulated reward to be optimized is often a very rugged function, and gradient methods are impaired by many local optimizers. We demonstrate, in an experimental setting, that incorporating an intrinsic reward can smoothen the optimization landscape while preserving the global optimizers of interest. We show that policy gradient optimization for locomotion in a complex morphology is significantly improved when supplementing the extrinsic reward by an intrinsic reward defined in terms of the mutual information of time consecutive sensor readings.

Connected through hyperedge HE-1 (induced by source paper "mega reward achieving human level play without extrinsic rewards"):
  with members:
    - "fast task inference with variational intrinsic successor features"
    - "diversity driven extensible hierarchical reinforcement learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "diversity is all you need learning skills without a reward function"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"

Connected through hyperedge HE-2 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-3 (induced by source paper "factorized mutual information maximization"):
  with members:
    - "large scale study of curiosity driven learning"
    - "deep variational information bottleneck"
    - "a theory of cheap control in embodied systems"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "multivariate information bottleneck"
    - "finding the maximizers of the information divergence from an exponential family"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-4 (induced by source paper "curiosity driven reinforcement learning with homeostatic regulation"):
  with members:
    - "a unified bellman equation for causal information and value in markov decision processes"
    - "curiosity driven exploration by self supervised prediction"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "continuous control with deep reinforcement learning"
    - "empowerment for continuous agent environment systems"
    - "planning to be surprised optimal bayesian exploration in dynamic environments"

Connected through hyperedge HE-5 (induced by source paper "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"):
  with members:
    - "a unified bellman equation for causal information and value in markov decision processes"
    - "curiosity driven exploration by self supervised prediction"
    - "variational intrinsic control"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "vime variational information maximizing exploration"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "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"
    - "linear combination of one step predictive information with an external reward in an episodic policy gradient setting a critical analysis"

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)