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: "higher coordination with less control a result of information maximization in the sensorimotor loop"
  Abstract: This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, non-communicating segments. The comparison of the results shows that maximizing the predictive information per wheel leads to a higher coordinated behavior of the physically connected robots compared to a maximization per robot. Another focus of this paper is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those of shorter length and more complex controllers. The reason is found and discussed in the information-geometric interpretation of the learning process.

Connected through hyperedge HE-1 (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-2 (induced by source paper "factorized mutual information maximization"):
  with members:
    - "large scale study of curiosity driven learning"
    - "deep variational information bottleneck"
    - "information theoretically aided reinforcement learning for embodied agents"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "evaluating morphological computation in muscle and dc motor driven models of hopping movements"):
  with members:
    - "a theory of cheap control in embodied systems"
    - "quantifying morphological computation based on an information decomposition of the sensorimotor loop"
    - "the umwelt of an embodied agent a measure theoretic definition"
    - "quantifying unique information"
    - "quantifying morphological computation"

Connected through hyperedge HE-4 (induced by source paper "quantifying morphological computation based on an information decomposition of the sensorimotor loop"):
  with members:
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "bivariate measure of redundant information"
    - "quantifying morphological computation"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-5 (induced by source paper "a theory of cheap control in embodied systems"):
  with members:
    - "quantifying morphological computation based on an information decomposition of the sensorimotor loop"
    - "geometry and expressive power of conditional restricted boltzmann machines"
    - "quantifying morphological computation"
    - "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"

Connected through hyperedge HE-6 (induced by source paper "quantifying emergent behavior of autonomous robots"):
  with members:
    - "novel plasticity rule can explain the development of sensorimotor intelligence"
    - "guiding designs of self organizing swarms interactive and automated approaches"
    - "empowerment an introduction"
    - "information driven self organization of complex robotic behaviors"

Connected through hyperedge HE-7 (induced by source paper "novel plasticity rule can explain the development of sensorimotor intelligence"):
  with members:
    - "information driven self organization of complex robotic behaviors"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

Connected through hyperedge HE-8 (induced by source paper "towards designing artificial universes for artificial agents under interaction closure"):
  with members:
    - "quantifying morphological computation"

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)