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"
  Abstract: The field of embodied intelligence emphasises the importance of the morphology and environment with respect to the behaviour of a cognitive system. The contribution of the morphology to the behaviour, commonly known as morphological computation, is well-recognised in this community. We believe that the field would benefit from a formalisation of this concept as we would like to ask how much the morphology and the environment contribute to an embodied agent’s behaviour, or how an embodied agent can maximise the exploitation of its morphology within its environment. In this work we derive two concepts of measuring morphological computation, and we discuss their relation to the Information Bottleneck Method. The first concepts asks how much the world contributes to the overall behaviour and the second concept asks how much the agent’s action contributes to a behaviour. Various measures are derived from the concepts and validated in two experiments that highlight their strengths and weaknesses.

Connected through hyperedge HE-1 (induced by source paper "action and perception for spatiotemporal patterns"):
  with members:
    - "towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems"
    - "motility at the origin of life its characterization and a model"
    - "detecting emergent processes in cellular automata with excess 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"
    - "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"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-3 (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"
    - "nonnegative decomposition of multivariate information"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "changing the environment based on empowerment as intrinsic motivation"):
  with members:
    - "empowerment an introduction"
    - "empowerment for continuous agent environment systems"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "towards designing artificial universes for artificial agents under interaction closure"):
  with members:
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-7 (induced by source paper "linear combination of one step predictive information with an external reward in an episodic policy gradient setting a critical analysis"):
  with members:
    - "information driven self organization of complex robotic behaviors"
    - "planning to be surprised optimal bayesian exploration in dynamic environments"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

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)