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: "information theoretic interpretation of quantum formalism"
  Abstract: We present an information-theoretic interpretation of quantum formalism based on a Bayesian framework and free of any additional axiom or principle. Quantum information is merely construed as a technique of statistical estimation for analyzing a logical system subject to classical constraints, regardless of the specific variables used. The problem is initially formulated in a standard Boolean algebra involving a particular set of working variables. Statistical estimation is to express the truth table in terms of likelihood probability instead of the variables themselves. The constraints are thus converted into a Bayesian prior. This method leads to solving a linear programming problem in a real-valued probability space. The complete set of alternative Boolean variables is introduced afterwards by transcribing the probability space into a Hilbert space, thanks to Gleason's theorem. This allows to completely recover standard quantum information and provides an information-theoretic rationale to its technical rules. The model offers a natural answer to the major puzzles that base quantum mechanics: Why is the theory linear? Why is the theory probabilistic? Where does the Hilbert space come from? Also, most of the paradoxes, such as entanglement, contextuality, nonsignaling correlation, measurement problem, etc., find a quite trivial explanation, while the concept of information conveyed by a wave vector is clarified. We conclude that quantum information, although dramatically expanding the scope of classical information, is not different from the information itself and is therefore a universal tool of reasoning.

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)