human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "evaluating morphological computation in muscle and dc motor driven models of hopping movements"):
  Abstract: In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be performed by the brain. Exploiting environmental and morphological properties is an important feature of embodied systems. The main reason is that it allows to significantly reduce the controller complexity. An important aspect of morphological computation is that it cannot be assigned to an embodied system per se, but that it is, as we show, behavior- and state-dependent. In this work, we evaluate two different measures of morphological computation that can be applied in robotic systems and in computer simulations of biological movement. As an example, these measures were evaluated on muscle and DC-motor driven hopping models. We show that a state-dependent analysis of the hopping behaviors provides additional insights that cannot be gained from the averaged measures alone. This work includes algorithms and computer code for the measures.

Includes members:
  Member "a theory of cheap control in embodied systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factorized mutual information maximization")
  Member "quantifying morphological computation based on an information decomposition of the sensorimotor loop":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computing the unique information")
  Member "the umwelt of an embodied agent a measure theoretic definition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop")
  Member "quantifying unique information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "quantifying morphological computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action and perception for spatiotemporal patterns")
  Member "higher coordination with less control a result of information maximization in the sensorimotor loop":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment")

Question: Please tell me which class the source paper 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)