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: "the umwelt of an embodied agent a measure theoretic definition"
  Abstract: We consider a general model of the sensorimotor loop of an agent interacting with the world. This formalises Uexkull’s notion of a function-circle. Here, we assume a particular causal structure, mechanistically described in terms of Markov kernels. In this generality, we define two \(\sigma \)-algebras of events in the world that describe two respective perspectives: (1) the perspective of an external observer, (2) the intrinsic perspective of the agent. Not all aspects of the world, seen from the external perspective, are accessible to the agent. This is expressed by the fact that the second \(\sigma \)-algebra is a subalgebra of the first one. We propose the smaller one as formalisation of Uexkull’s Umwelt concept. We show that, under continuity and compactness assumptions, the global dynamics of the world can be simplified without changing the internal process. This simplification can serve as a minimal world model that the system must have in order to be consistent with the internal process.

Connected through hyperedge HE-1 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop"):
  with members:
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "accelerating empowerment computation with uct tree search"
    - "universal reinforcement learning algorithms survey and experiments"
    - "minimum information lqg control part ii retentive controllers"
    - "nonparametric general reinforcement learning"
    - "variational intrinsic control"
    - "generalized thompson sampling for sequential decision making and causal inference"
    - "empowerment an introduction"

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"
    - "quantifying unique information"
    - "quantifying morphological computation"
    - "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.IT(Information Theory)