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: "a theory of cheap control in embodied systems"
  Abstract: We present a framework for designing cheap control architectures of embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent’s embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the classical non-embodied universal approximation. To exemplify our approach, we present a detailed quantitative case study for policy models defined in terms of conditional restricted Boltzmann machines. In contrast to non-embodied universal approximation, which requires an exponential number of parameters, in the embodied setting we are able to generate all possible behaviors with a drastically smaller model, thus obtaining cheap universal approximation. We test and corroborate the theory experimentally with a six-legged walking machine. The experiments indicate that the controller complexity predicted by our theory is close to the minimal sufficient value, which means that the theory has direct practical implications.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "nmode neuro module evolution"):
  with members:
    - "linear combination of one step predictive information with an external reward in an episodic policy gradient setting a critical analysis"

Connected through hyperedge HE-3 (induced by source paper "evaluating morphological computation in muscle and dc motor driven models of hopping movements"):
  with members:
    - "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"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-4 (induced by source paper "geometry and expressive power of conditional restricted boltzmann machines"):
  with members:
    - "conditional restricted boltzmann machines for structured output prediction"

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.SY(Systems and Control)