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: "how information transfer works interpretation of information contents in bayes theorem understanding negative information"
  Abstract: In a given space of models or hypothesis the individual information content of each of them is considered as opposed to the Shannon entropy that measures the average information content of the mentioned space. In particular expressing Bayes Theorem in terms of the information contents associated to its probabilities allows understanding how bits of information, introduced in the system by an observation, are transferred to each of the models in the space. It is shown how, from a single observation not one, but two causal information sources are generated: the Information Content Associated to the Evidence that always introduces positive information, and the Information Content Associated to the Bayes Likelihood that always introduces negative bits; therefore the evidence contributes to increase the probability of occurrence of the model and the likelihood to decrease it; depending on the net value of the difference between these two mentioned information contents, the information that arrives to a given model will be positive or negative. Thus, we propose a novel metric, given by the difference of the two mentioned information contents called transfer information content which measures the information transferred to each of the single models in the space. The resolution of the Monty Hall Problem (MHP) and some of its variants in the Information Theory framework proposed allows to confirm the validity of the formulas derived and to understand the counterintuitive and theoretically problematic concept of negative information. The implications of the concepts introduced in terms of information transfer to the emergent field of Local Information Dynamics, to Computational Neuroscience (particularly to Directed Information Theory and Neural Coding) are proposed as further work.

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)