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: "unifying formalism for multivariate information related measures mobius operators on subset lattices"
  Abstract: Information-related measures are important tools of multi-variable data analysis, as measures of dependence among variables and the description of order in biological and physical systems. Informtion-related measures, like marginal entropies, mutual / interaction / multi-information, are also relevant to the theory of complexity of systems and biological data analysis. The mathematical properties and relationships among these measures are therefore of significant interest. A number of interesting relations between common information measures include the duality relations based on M\"obius inversion on lattices, which are the direct consequence of the symmetries of the lattices (subsets ordered by inclusion) of the sets of variables. In this paper we define operators on functions on these lattices that map the functions into one another, called M\"obius operators. We show that these operators define a simple group isomorphic to the symmetric group S3. Relations among the set of functions on the lattice are transparently expressed in terms of this operator algebra, and when applied to the information measures, can be used to describe relationships among measures useful in data analysis, including conditional probabilities. We describe a direct relation between sums of conditional log-likelihoods and previously defined dependency/complexity measures. The algebra can be generalized by defining operators for all possible lattice reference elements (subsets), which yields a general set of relationships more extensive and inclusive than the usual M\"obius inversion relations. This formalism provides a fundamental unification of information-related measures. Isomorphism of all distributive lattices with the subset lattice implies broad potential application of these results.

Connected through hyperedge HE-1 (induced by source paper "the group theoretic roots of information i permutations symmetry and entropy"):
  with members:
    - "maximum entropy on compact groups"
    - "distribution of mutual information from complete and incomplete data"

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)