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: "quantifying morphological computation based on an information decomposition of the sensorimotor loop"
  Abstract: The question how an agent is affected by its embodiment has attracted growing attention in recent years. A new field of artificial intelligence has emerged, which is based on the idea that intelligence cannot be understood without taking into account embodiment. We believe that a formal approach to quantifying the embodiment's effect on the agent's behaviour is beneficial to the fields of artificial life and artificial intelligence. The contribution of an agent's body and environment to its behaviour is also known as morphological computation. Therefore, in this work, we propose a quantification of morphological computation, which is based on an information decomposition of the sensorimotor loop into shared, unique and synergistic information. In numerical simulation based on a formal representation of the sensorimotor loop, we show that the unique information of the body and environment is a good measure for morphological computation. The results are compared to our previously derived quantification of morphological computation.

Connected through hyperedge HE-1 (induced by source paper "computing the unique information"):
  with members:
    - "disentangled representations via synergy minimization"
    - "the blackwell relation defines no lattice"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "a review of feature selection methods based on mutual information"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "bivariate measure of redundant information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-2 (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"
    - "the umwelt of an embodied agent a measure theoretic definition"
    - "quantifying unique information"
    - "quantifying morphological computation"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-3 (induced by source paper "synergy redundancy and common information"):
  with members:
    - "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "exact common information"
    - "quantifying synergistic mutual information"
    - "intersection information based on common randomness"
    - "quantifying redundant information in predicting a target random variable"
    - "bivariate measure of redundant information"

Connected through hyperedge HE-4 (induced by source paper "a theory of cheap control in embodied systems"):
  with members:
    - "geometry and expressive power of conditional restricted boltzmann machines"
    - "quantifying morphological computation"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"
    - "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"

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)